Submitted 3 May 2022

Accepted 22 June 2022

Published 22 July 2022

Corresponding author

Fabrizio Montesi,

fmontesi@imada.sdu.dk

Academic editor

Muhammad Aleem

Additional Information and

Declarations can be found on

page 35

DOI 10.7717/peerj-cs.1037

2022 Giallorenzo et al.

Distributed under

Creative Commons CC-BY 4.0

OPEN ACCESS

Ephemeral data handling in microservices

with Tquery

Saverio Giallorenzo

1,2

, Fabrizio Montesi

, Larisa Safina

and Stefano Pio Zingaro

1,2

Università di Bologna, Bologna, Italy

INRIA, Sophia Antipolis, France

University of Southern Denmark, Odense, Denmark

INRIA, Lille, France

ABSTRACT

The adoption of edge and fog systems, along with the introduction of privacy-

preserving regulations, compel the usage of tools for expressing complex data queries

in an ephemeral way. That is, queried data should not persist. Database engines

partially address this need, as they provide domain-specific languages for querying

data. Unfortunately, using a database in an ephemeral setting has inessential issues

related to throughput bottlenecks, scalability, dependency management, and security

(e.g., query injection). Moreover, databases can impose specific data structures and

data formats, which can hinder the development of microservice architectures that

integrate heterogeneous systems and handle semi-structured data. In this article,

we present Jolie/Tquery, the first query framework designed for ephemeral data

handling in microservices. Jolie/Tquery joins the benefits of a technology-agnostic,

microservice-oriented programming language, Jolie, and of one of the most widely-used

query languages for semi-structured data in microservices, the MongoDB aggregation

framework. To make Jolie/Tquery reliable for the users, we follow a cleanroom

software engineering process. First, we define Tquery, a theory for querying semi-

structured data compatible with Jolie and inspired by a consistent variant of the

key operators of the MongoDB aggregation framework. Then, we describe how we

implemented Jolie/Tquery following Tquery and how the Jolie type system naturally

captures the syntax of Tquery and helps to preserve its invariants. To both illustrate

Tquery and Jolie/Tquery, we present the use case of a medical algorithm and build

our way to a microservice that implements it using Jolie/Tquery. Finally, we report

microbenchmarks that validate the expectation that, in the ephemeral case, using

Jolie/Tquery outperforms using an external database (MongoDB, specifically).

Subjects Distributed and Parallel Computing, Theory and Formal Methods, Programming

Languages, Internet of Things

Keywords Microservices, Jolie, Semi-structured data, Ephemeral data, Edge computing, Fog

computing, Formal methods, Service-oriented computing, Query languages, e-Health

INTRODUCTION

Background

Modern applications that make use of Edge Computing (Shi et al., 2016) and the Internet

of Things (IoT for short) (Baker, Xiang & Atkinson, 2017) are increasingly developed

as systems of microservices: independently executable components that communicate

How to cite this article Giallorenzo S, Montesi F, Safina L, Zingaro SP. 2022. Ephemeral data handling in microservices with Tquery.

PeerJ Comput. Sci. 8:e1037 http://doi.org/10.7717/peerj-cs.1037

via message passing (Dragoni et al., 2017). These systems typically have to deal with

the continuous acquisition, processing, and distribution of semi-structured data. Over

the last decade, the need for such data handling has contributed significantly to the

adoption of document-oriented querying frameworks (Leavitt, 2010), like the MongoDB

aggregation framework (MongoDB Inc., 2022)—and especially so in settings where Cloud

Computing (Armbrust et al., 2010) is involved as well.

Recently, the necessity for careful data handling and the introduction of data protection

regulations like the GDPR (Van Alsenoy, 2019) has highlighted the importance of handling

ephemeral data (Shein, 2013). That is, in order to limit the circulation of data, applications

should quickly process information without relying on persistency.

Ephemeral data handling is particularly relevant in scenarios where privacy is

important (Mostert et al., 2016), for example eHealth (electronic systems that support

healthcare) (Baker, Xiang & Atkinson, 2017), because it ensures by construction that data

is automatically discarded unless the developers manually specifies otherwise. However,

collecting and querying data with general-purpose languages in these contexts is often time

consuming and error-prone (Reda, Piccinini & Carbonaro, 2018; Ma, Wang & Chu, 2013).

In particular:

1. The implementation of query pipelines can quickly become complicated without

proper abstractions.

2. Data might come from heterogeneous sources and in different data formats.

To solve the first issue (querying), developers typically include in their systems

components that offer dedicated query languages (Cheney, Lindley & Wadler, 2013). For

semi-structured data, a popular approach is to store data in a MongoDB instance (MongoDB

Inc., 2018b), and then to use the MongoDB aggregation framework to perform queries.

As for the second issue (heterogeneity), developers can get support from programming

languages or frameworks in which programs abstract from the concrete representation of

data on the wire. Then, data is converted into the appropriate format and communicated

through the appropriate protocol at runtime. Jolie is a (micro)service-oriented language

designed to offer this capability (Montesi, Guidi & Zavattaro, 2014). A Jolie service can

type, communicate, and manipulate semi-structured data under a unifying model that

abstracts from data formats and communication protocols. Then, the program can be

reused with different deployment instructions, which inform the Jolie engine of how

data should be formatted (binary representations, JSON, XML, etc.) and communicated

(using HTTP, SOAP, etc.) (Montesi, 2016). Jolie instructions can further be composed

in workflows (Gabbrielli, Giallorenzo & Montesi, 2014); a feature that simplifies the

programming of data collection and distribution in IoT and edge environments (Gabbrielli

et al., 2019).

The Problem

Ideally, a tool for ephemeral data handling in microservices would give us the best of

the MongoDB aggregation framework and of the Jolie programming language: a query

framework designed for semi-structured data and a language for working in heterogeneous

environments.

Giallorenzo et al. (2022), PeerJ Comput. Sci., DOI 10.7717/peerj-cs.1037 2/40

An obvious attempt at achieving what we want is to just ‘‘stick together’’ MongoDB

with Jolie, in the sense of deploying a Jolie service in the company of a MongoDB instance.

Unfortunately, this approach runs into issues:

Dependency An external Database Management System (DBMS) like MongoDB is an

additional standalone component that needs to be installed, deployed,

and maintained. As with any software dependency, this exposes the

applications to challenges of version incompatibility (Jang, 2006).

Security The companion DBMS is subject to weak security configurations (Brian

Krebs, 2017) and query injections, increasing the attack surface of the

application. This is a typical problem in microservices-with-database

deployments where usually the microservice composes queries by

assembling external inputs as strings, which is the main vector for

query injections (Ron, Shulman-Peleg & Puzanov, 2016).

Inconsistency The key features of the MongoDB aggregation framework have only

recently been formally understood, and some present idiosyncrasies

related to implementation that do not make sense for a clean, abstract

model (Botoeva et al., 2018).

Performance The communication channel between the MongoDB instance and the

Jolie service can become a bottleneck, introducing the usual performance

issues of database connections (Visveswaran, 2000). This is common

in microservices-with-database scenarios where the overheads of

establishing database connections can limit the performance of the

whole component (and techniques, like managing pools of persistent

database connections, are partial solutions (Visveswaran, 2000) that make

the logic of the microservices more involved). Data format conversions in

these communications contribute to overhead as well, together with the

necessary measures to ensure ephemerality (post-query data deletion).

Our solution

We propose the integration of relevant MongoDB data-query operators in Jolie. Our

solution avoids the issues above: Dependency, since there is not anymore a database that

we need to install and maintain; Security, because shedding the database removes risks

from weak security configurations and, since the queries are part of the language (and not

simply strings that we forward to the database engine), we also lower the exposition to

query injections; Inconsistency, by building upon previous work on the formalisation of a

consistent data-query theory of MongoDB (Botoeva et al., 2016); Performance, since there

is no database involved, we avoid the overhead of: passing the data to and from the database;

possible data-format conversions; bottlenecks due to pools of database connection channels

(and possible bugs linked to their management), and of ensuring ephemerality.

This article

We present two main contributions. The first one is a formal model of a query language

for semi-structured data, called Tquery. The second is an implementation of Tquery, called

Giallorenzo et al. (2022), PeerJ Comput. Sci., DOI 10.7717/peerj-cs.1037 3/40

Jolie/Tquery, which is the first query framework designed for ephemeral data handling in

microservices. Jolie/Tquery addresses the problem by joining the benefits of Jolie and of

the MongoDB aggregation framework: data can be collected from heterogeneous sources

and then be queried in local memory by using pipelines of operations on semi-structured

data.

The development of Jolie/Tquery is inspired by cleanroom software engineering. In

particular, we have implemented our framework from scratch, starting from a formal

model of its operators and their semantics. Our main contributions are described in the

following.

Formal Specification We define Tquery, a theory for querying semi-structured data

compatible with Jolie. Tquery provides the key operators of the MongoDB aggregation

framework (match, unwind, project, group, and lookup), but reformulated for Jolie data

structures and their accompanying syntax of paths for data traversal.

Implementation We develop Jolie/Tquery, an implementation of Tquery in the form

of a Jolie package that can be used in services. Jolie/Tquery is lightweight: the entire

compiled package consists of less than 100 kb. The implementation consists of two parts:

an Application Programming Interface (API) to construct and run query pipelines, which

defines the syntax of Tquery operators in terms of Jolie types; and an implementation of the

API that follows the semantics given in Tquery. Jolie comes with an engine that supports

implementing Jolie APIs with different languages (Montesi, 2016). In our case, Jolie/Tquery

is implemented in Java. Jolie applications can use Jolie/Tquery by passing data in local

memory and using native Jolie structures, which avoids the aforementioned issues. At the

same time, Jolie applications can use Jolie’s capabilities for integrating with heterogeneous

components to collect and distribute data.

Evaluation We illustrate the expressivity of Jolie/Tquery by using it to implement a use

case from eHealth: a detection system for encephalopathy based on a proposal by Vigevano

& Liso (2018). We then carry out microbenchmarks to validate the expectation that using

Jolie/Tquery, being an in-memory query framework, outperforms using an external

database management system (MongoDB specifically).

The article is structured as follows. ‘Related Work’ covers the related work. ‘Overview

and Running Example’ illustrates the Tquery with the running example from the eHealth.

‘The Tquery Formalisation’ introduces formalisation of Tquery. ‘Implementation’ presents

implementation of Tquery as a microservice written in Jolie programming language.

‘Benchmarks’ provides the benchmarks comparing the Tquery with the MongoDB.

‘Discussion and Conclusion’ drives conclusions.

This is the journal version of Giallorenzo et al. (2019), a short conference article where

we presented preliminary ongoing work about the implementation of Jolie/Tquery.

RELATED WORK

Jolie/Tquery is the first implementation from scratch of a formally-specified, document-

oriented query framework. Our formal model, Tquery, stands on the shoulders of

MQuery (Botoeva et al., 2018), the first formal model of query operators for JSON data

Giallorenzo et al. (2022), PeerJ Comput. Sci., DOI 10.7717/peerj-cs.1037 4/40

structures. MQuery formalises the key operators of the MongoDB aggregation framework,

dispensing from some unnecessary idiosyncrasies that can lead to counterintuitive

behaviour. Tquery inherits this good feature—the reader interested in the technical

differences w.r.t. the MongoDB aggregation framework can consult (Botoeva et al., 2016,

Appendix C). The key difference between this work and Botoeva et al. (2018) is that Tquery

comes with an implementation, whereas Botoeva et al. (2018) investigated the theoretical

expressivity of the MQuery operators w.r.t. relational algebra and their complexity.

Tquery adopts the same operators but reformulated to be compatible with the Jolie data

model (by adopting arrays instead of unordered forests for document collections). The

semantics of our operators is also specified differently: while Tquery’s operators follow

the same intuition of the operators in MQuery, we give our semantics specifying how

operations can be computed. For example, we do not rely on existential quantification

and all our definitions are given by recursion on the structures of inputs. We believe that

formalisation efforts like MQuery and Tquery are important: during the development

of our implementation, we found having a reference formal model helpful to clarify the

expected behaviour of operators and what tests we should write.

Jolie has been used in several domains that require ephemeral data handling, including

smart mobility (Callegati et al., 2017), IoT (Gabbrielli et al., 2018), integration components

in document management systems (Maschio, 2019), and media content (Maschio, 2017).

However, due to the lack of an appropriate query framework, the query logic has been

implemented manually with a general-purpose computation language (the computation

layer of Jolie). Because it guarantees that data gets discarded (ephemerality) and it provides

an expressive set of compositional query operators, Jolie/Tquery offers a better alternative

for writing data-intensive Jolie microservices. Moreover, since every Jolie program is a

composition of services, adapting a program to offload parts of its computations to remote

nodes is simple (it mainly regards the reconfiguration of how services are deployed). This,

in unison with the fact that Jolie/Tquery operators are stateless, simplifies the task of

splitting Jolie/Tquery heavy-weight or computation-intensive queries over multiple nodes.

Other solutions that offer semi-structured data querying in separate services include

MongoDB (MongoDB Inc., 2018b) and CouchDB (Apache, 2005); however, these are

DB-based solutions that fall into the category of deployments that we deem unfit for the

case of ephemeral data-handling. Moreover, these do not come with a formal model which,

e.g., one can use to reason about the semantics of the implementation and to check its

consistency (like Botoeva et al. (2016) demonstrated for MongoDB).

There exist works on the integration of relational query frameworks with general-purpose

programming languages, including: object-relation mapping frameworks (ORMs), which

map objects to database entities (Fussel, 1997); Opaleye, a Haskell DSL for generating

PostgreSQL commands (Ellis, 2014); and LINQ (Meijer, Beckman & Bierman, 2006), which

provides query operators targeting SQL tables and XML structures for .NET languages.

Tquery could be a reference to implement similar frameworks for semi-structured data in

these languages. A convenient feature of Jolie/Tquery is that all its queries can work with

any data format that Jolie can handle: Jolie automatically converts data in different formats

Giallorenzo et al. (2022), PeerJ Comput. Sci., DOI 10.7717/peerj-cs.1037 5/40

(including JSON, XML, and some binary formats) to its abstract data model (Montesi,

Guidi & Zavattaro, 2014; Montesi, 2016).

As we are going to exemplify in the next section, a typical use case for semi-structured data

handling and Jolie/Tquery is the reactive processing of events. Stream-processing languages

have been explored for similar tasks, but they feature different kinds of primitives and are

usually not based on semi-structured data.

The landscape of stream-processing languages is quite wide, e.g., data-centric (Chen et al.,

2000; Barbieri et al., 2009), time- or hardware-constrained execution-centric (Caspi et al.,

1987; Hirzel, Schneider & Gedik, 2017; Tommasini et al., 2019), focussed on the relational-

or document-oriented (Chen et al., 2000; Diao et al., 2002; Mendell et al., 2012) approach.

In particular, SQL-based stream-processing languages (Esteves et al., 2017; Babu & Widom,

2001) recently gained popularity in industry (thanks to the familiarity of programmers with

the SQL language), with commercial tools such as Apache Flink (Apache, 2022a), Apache

Kafka (KSQL) (Narkhede, 2017), Apache Samza (Apache, 2022b), Apache Storm (Apache,

2022c), WSO2 Stream Processor (WSO2, 2022), Siddhi (Siddhi Streaming SQL) (Siddhi,

2022). We deem StreamQL (Kong & Mamouras, 2020) the work closest to Jolie/Tquery.

This is a query language for efficiently processing IoT data streams. The StreamQL Engine

is implemented as a lightweight Java library and does not depend on the external engine.

However, StreamQL is a functional language that is based on formal semantics residing on

the class of monotone functions over streams. It works with the typical functional primitives

on list-based data, supporting a variety of operators that simplify stream-processing at the

level of data aggregation (filtering, windowing, etc.) and data-flow control (e.g., parallel

composition). StreamQL does not handle explicitly semi-structured document-oriented

data and requires additional processing for data translation, while Jolie/Tquery handles it

natively(tree-shaped data simplifies integration with Jolie). Unlike Jolie/Tquery, StreamQL

has built-in primitives for temporal control typical for data streaming languages. In

Jolie/Tquery time contracts can be implemented by adding information to the data

structures and need to be managed explicitly by the programmer. Widening our scope, we

deem two works, CQL (Arasu, Babu & Widom, 2006) and EQL (Elasticsearch, 2022), close

to Jolie/Tquery. CQL is a declarative streaming SQL-based query language, implemented in

the STREAM DSMS (Arasu et al., 2016) with data captured with sliding windows (Babcock

et al., 2002) based on time-(e.g., update the data every 30 s) and data-related conditions

(e.g., capture the data as soon as it arrives). EQL (Elasticsearch, 2022) is an event-based data

manipulation library developed in Python. Similarly to CQL, EQL expects data to follow an

event-oriented schema. Interestingly, EQL provides a query-composition operator similar

to the one provided by Jolie/Tquery (see ‘Extending Jolie/Tquery with query pipelines’).

Both CQL and EQL, being SQL-based, work on tuples of data rather than semi-structured

documents as Jolie/Tquery does—e.g., one needs to convert a JSON document into tuples

of data before using CQL/EQL.

Finally, Ballerina (Oram, 2019) is a language for the development of microservices close

to Jolie, developed by WSO2, that equips SQL-like query operators to process data and

events. The differences with Jolie/Tquery include the relational nature of the operators,

Giallorenzo et al. (2022), PeerJ Comput. Sci., DOI 10.7717/peerj-cs.1037 6/40

which requires the user to translate values between document- and tuple-shaped data when

applying/using the data from the queries, and the lack of a formal reference.

OVERVIEW AND RUNNING EXAMPLE

In this section, we illustrate our proposal with an eHealth use case, showing the definition

of a diagnostic algorithm as a composition of Tquery’s operators. We deem this area of

application apt to illustrate Tquery for two main reasons.

First, since medical diagnostic algorithms are usually expressed through declarative

or high-level imperative instructions, having high-level, declarative operators for data

handling narrows the gap between definitions and implementations and helps in both

translating and checking their correctness. Indeed, more and more studies emerged

proposing non-intrusive, affordable yet accurate diagnostic systems based on data collected

from heterogeneous sources such as user-inputted data, smartphones, wearables, and

cameras (Purohit et al., 2020). An emblematic example of this phenomenon is the recent

proposal by Hirten et al. (2020), where the authors defined and demonstrated the efficacy

of a diagnostic algorithm to identify and predict SARS-CoV-2 (aka COVID-19) infections,

reporting promising predictive ability to identify infection days before the diagnosis

through nasal-swab testing. Here, we focus on a simpler-yet-comprehensive diagnostic

algorithm defined by Vigevano & Liso (2018) to detect cases of encephalopathy.

Second, the inherent ephemerality of Tquery programs caters to the principles of secrecy

and obliviousness of data—the data handled by a Tquery program is automatically deleted

from memory—in the healthcare sector. This approach is frequently summarised by the

motto ‘‘the data never leave the hospital’’ and it is compliant with the current regulations

on data protection (e.g., GDPR (Rose, 2014)).

In the remainder of the article, we use the diagnostic algorithm by Vigevano & Liso (2018)

to illustrate the formal semantics of Tquery. Here, we focus on the overall definition of the

parts of the algorithm and how we can map them to a combination of Tquery operators

acting on and merging data from different sources. Then, in ‘The Tquery Formalisation’,

we return on the single instructions that make up the algorithm presented here and show

the step-by-step output of Tquery operators, following from the specification of their

semantics.

An encephalopathy diagnostic algorithm

Taking inspiration from Vigevano & Liso (2018), we focus on the aggregation of two early

markers to detect encephalopathy: fever in the last 72 h and lethargy in the last 48 h. Those

data are collectable by commercially-available smart-watches and smart-phones (Bunn et

al., 2018): body temperature and sleep quality.

Tquery defines operators over tree-like data structures, formally defined in ‘Data

structures: trees and paths’. To keep this example compact, it is sufficient that the reader

has some familiarity with data formats like XML (Bray et al., 2000) and JSON (Crockford,

2006) documents. Specifically, here we use a subset of the JSON format where a tree is

represented by a pair of brackets { }, which enclose a set of ordered pairs, each linking a

Giallorenzo et al. (2022), PeerJ Comput. Sci., DOI 10.7717/peerj-cs.1037 7/40

label (unquoted) to an array, whose content is enclosed within square brackets [ ]. Arrays

can either contain trees or primitive values (string, integer, etc.).

As an example of the format above, we report in Listing 1 code snippets exemplifying

the shape of the two data structures used in the example: the first (Lines 2–5) carries the

temperature and heart-rates, the second holds the sleep logs (Lines 7–16) (Thurman et al.,

2018).

At Lines 2–5, for each date we have an array of detected temperatures (t) and heart-rates

(hr). At Lines 7–16, to each year (y) it corresponds an array of monthly (M) measures, to a

month (m), an array of daily (D) logs, and to a day (d), an array of logs (L), each representing

a sleep session with its start (s), end(e), and quality (q).

1 // representation of the tmp data structure

2 [ {date :[ 2020112 7 ] , t:[ 37 ], hr : [ 64 ] } ,

3 { date : [ 2 0 2 01128 ] , t: [ 36 ], hr : [ 66 ] } ,

4 { date : [ 2 0 2 01129 ] , t: [ 36 ], hr : [ 65 ] } ,

5 { date : [ 2 0 2 01130 ] , t: [ 37 ], hr : [ 67 ] } ]

6 // representation of the sl d ata s t r ucture

7 [ {y : [ 2 020 ],

8 M: [ { m:[ 11 ],

9 D: [ { d:[ 27 ], L:[{ s : [ ' 23:33 '], e:[ ' 07:0 4 '], q:[ ' poor ']}] } ,

10 {d : [ 28 ] , L: [ { s:[ ' 2 1:13 '] , e:[ ' 0 9:34 '] , q: [ ' good ']} ] } ,

11 {d : [ 29 ] , L: [ { s:[ ' 2 1:01 '] , e:[ ' 0 3:12 '] , q: [ ' good ']} ,

12 {s : [ ' 03:36 ' ] , e: [ ' 09 :58 ' ] , q: [ ' g ood '] } ] },

13 {d : [ 30 ] , L: [ { s:[ ' 2 0:33 '] , e:[ ' 0 1:14 '] , q: [ ' poor ']} ,

14 {s : [ ' 01:32 ' ] , e: [ ' 06 :15 ' ] , q: [ ' g ood '] } ] }

15 ]}] } ]

16 } ]

Listing 1: Snippets of biometric (Lines 2–5) and sleep logs (Lines 7–16) data.

To implement the algorithm for detecting encephalopathy, we need to integrate

with two functionalities provided by the Hospital IT infrastructure: detectFever and

detectEncephalopathy. The former accepts data of the shape:

[ { t :[ 36 , 37 , 38 ], patient_id : [ ' xyz ' ] } ]

where t contains the array of measured temperatures in the last three days and patient_id

the identifier for the patient in the Hospital IT infrastructure. The latter accepts the format:

[ { temperatures : [ 36 , 37 , 38 ] , pat i e n t _ id : [ ' xyz ' ],

qua l ity : [ ' good ' , ' po or ' ] } ]

where temperatures contains the measured temperatures in the last three days, patient_id

contains the identifier for the patient in the Hospital IT infrastructure (essentially, this

is the same data found, respectively, under t and patient_id issued to the detectFever

functionality), and quality contains the recorded quality of sleep in the last two days.

Our focus in the last part of this section is to describe—by means of the Tquery

operators—a program that manipulates the biometric and sleep logs data in Listing 1

to integrate the functionalities detectFever and detectEncephalopathy and implement the

diagnostic algorithm.

Giallorenzo et al. (2022), PeerJ Comput. Sci., DOI 10.7717/peerj-cs.1037 8/40

Intuitively, a path is a sequence of node

labels of the shape A.B.C. Formally, cf.

‘Data structures: trees and paths’.

An overview of the Tquery oper ators

Before presenting the diagnostic algorithm, we give a brief and informal description

of the shape and effect of each Tquery operator (presented formally in ‘The Tquery

Formalisation’), as a reference to integrate the description of the example.

• the match operator µ, given an array and a match criterion returns the elements of the

array that satisfy the criterion, in their relative order from the input;

• the unwind operator ω takes as inputs an array and a path p.

The result of the application

is a new array containing the ‘‘unfolding’’ of the input array under the path, i.e., where

we take each element e from the input array, we find all values under p in e and, for

each value, we include in the new array a copy of e except it holds only that single value

under p;

• the project operator π, given an array and a projection expression, it returns a copy of

the original array with each element updated by the projection expression. Projection

expressions can move/rename and remove sub-parts from the elements, as well as insert

new ones;

• the group operator γ takes as inputs an array and two lists of paths: a grouping list and

an aggregation list. The result of the application is a new array where each element has

two properties: (i) it includes the combinations of distinct values from the set of values

found under the grouping paths among the elements in the input array; (ii) it aggregates

all the values found under the aggregation paths among the elements in the input array

which have been grouped by the same combination of values;

• the lookup operator λ joins two arrays, a ‘‘source’’ and an ‘‘adjunct’’ one, according

to the correspondence of values in their elements with respect to a source path and

an adjunct path. Besides those inputs, the operator requires a ‘‘destination’’ path. The

application of the operator returns a new array that contains all the elements resulting

from merging each element e

in the source array with the elements e

in the adjunct

array such that e

and e

hold the same values under the respective source and adjunct

paths. The resulting array contains all the elements from the source, each updated to

include, under the provided destination path, all path-matching elements from the

adjunct array.

Implementing the diagnostic algorithm with Jolie and Tquery

Here, on the data structures and operators described above, we define a Jolie microservice

(reported in Listing 2), which implements the handling of the data and the workflow of

the use-case diagnostic algorithm.

The example is broad enough to let us illustrate all the operators in Tquery and to

represent a real-world workflow, where, besides implementing the algorithm of interest,

we manipulate the data for system integration (e.g., by reshaping the data structures to

fit the service APIs we need to invoke). Note that, while in Listing 2 we hard-code some

data (e.g., integers representing dates like 20201128) for presentation purposes, we would

normally use parametrised variables.

Since we follow a formalisation-first approach to present Tquery, in Listing 2 we

interleave runnable Jolie code with the formal definition of the application of the involved

Giallorenzo et al. (2022), PeerJ Comput. Sci., DOI 10.7717/peerj-cs.1037 9/40

Tquery operators. When doing so, we use the highlighted, algorithmic notation ··· ← ···.

After having defined the formal semantics of the operators in ‘The Tquery Formalisation’,

we will present the actual implementation of the example in ‘Implementation’ using our

implementation of the Tquery operators in Jolie.

Note also that, while variables of the form patientData and tmp in Listing 2 conveniently

resemble variable symbols as found in Java or C, they are actually path applications on the

state of a Jolie program, which is a tree. Hence, the meaning of tmp reads ‘‘get the structure

pointed by path tmp in the current state of the program’’. In the example, when assigning

and passing values, we use the notation a and b.c to indicate the path traversal and retrieval

of the structure pointed by the respective paths a and b.c on the state of the Jolie program.

We instead use the notation a and b.c to indicate the passing of paths as parameters of

Tquery operators.

We now describe the diagnostic algorithm and how we use the Tquery operators to

implement it.

In Listing 2, at Line 1 we find the Jolie code of a request to an external service, provided

by the HospitalIT infrastructure. The service offers the functionality getPatientPseudoID

which, given some identifying patientData (acquired earlier), provides a pseudo-

anonymised identifier—needed to treat sensitive health data—saved in variable pseudoID.

At Line 2 we retrieve in the variable credentials the keys to access the physiological sensors

of the patient to obtain the biometric data (Listing 1, Lines 1–5) from the SmartWatch of

the patient, by invoking the functionality getMotionAndTemperature and storing the result

in tmp.

At Lines 3–5 we use the Tquery operators µ, γ , and π to extract the recorded

temperatures of the patient in the last 3 days/72 h. At Line 3 we use the match operator µ

to filter all the entries of the biometric data, keeping only those of the last 72 hours/3 days.

At Line 3, we aggregate the result of the µ (which replaced the previous value under tmp)

under the path t and discard the others. At Line 5, we use the project operator to include

in tmp the identifier of the patient, under patient _id.

At Line 6, we call the external functionality detectFever to analyse the temperatures and

check if the patient manifested any fever, storing the result in hasFever.

1 getPatien t P s e u d o I D @ Hospita l I T ( patientData ) ( ps e u doID )

2 getMo t i o n A n dTempe r a t u r e @ SmartWat c h ( credentials ) ( tmp )

3 tmp← µ( tmp , (date = 20201128 ∨ date =20201 1 2 9 ∨ date = 2 0 2 0 1130) )

4 tmp← γ ( tmp , (t ) , () )

5 tmp← π ( tmp , ( t , ps e udoID i patient_id ) )

6 detec t F e v e r @ H o s p i talIT ( tmp )( ha sFever )

7 if ( h a sFever ) {

8 getSleepPatt e r n s @ S m a r tPhone ( cr e d e n t i als )( sl )

9 sl← ω( sl , M .D.L )

10 sl← π ( sl , (y i year , M .m i month, M .D.d i day , M.D.L.q i quality ) )

11 sl← µ( sl , (year =2 020 ∧ month=11 ∧ (day =29 ∨ day =30) ) )

12 sl← γ ( sl , (quality ), () )

13 sl← π ( sl , (quality , ps eudoID i patient_id ) )

Giallorenzo et al. (2022), PeerJ Comput. Sci., DOI 10.7717/peerj-cs.1037 10/40

14 bs← λ( sl , patient_id , tmp , patient _id , temperatures )

15 bs← π ( bs , (quality , patient _id , temperatures.t i temperatures) )

16 detect E n c e p h alopathy @ H o spitalIT ( bs )

17 }

Listing 2: Snippet implementing the diagnostic algorithm.

After the analysis on the temperatures, if the patient hasFever (Line 7), we continue

testing for lethargy. To do that, at Line 8 we follow the same strategy described for Line 2

to pass the credentials to the functionality getSleepPatterns, used to collect the sleep logs

of the patient from her SmartPhone in sl.

Then, since the sleep logs are nested under years, months, and days, to filter the logs

relative to the last 48 hours/2 days, we first flatten the structure through the unwind ω

operator applied on the path M.D.L (Line 9). For each nested node (separated by the dot

in the path), ω generates a new data structure for each element in the array reached by that

node. Concretely, the array returned by the ω operator at Line 9 contains each sleep log

associated with the full date of the recording (year, month, and day), as shown below.

[{y : [ 2020 ], M : [{m : [ 11 ] , D:[ { d :[ 27 ],L : [ {s : [ ' 23: 33 '] , ... }] } ] }]} ,

{y : [ 2020 ],M : [{m : [ 11 ] , D:[ { d :[ 28 ], L : [{s : [ ' 21: 13 '], ... }]} ] } ]} ,

{y : [ 2020 ],M : [{m : [ 11 ] , D:[ { d :[ 29 ], L : [{s : [ ' 21: 01 '], ... }]} ] } ]} ,

{y : [ 2020 ],M : [{m : [ 11 ] , D:[ { d :[ 29 ], L : [{s : [ ' 03: 36 '], ... }]} ] } ]} ,

... ]

Given the new shape of sl, at Line 10 we modify the data structure with the project

operator π to simplify the subsequent commands: we rename the node y to year, we

move and rename the node M.m to month (bringing it at the same nesting level of year);

similarly, we move M .D.d, renaming it day, and we move M.D.L.q (the log of the quality

of the sleep), renaming it quality—M.D.L.s and M.D.L.e, not included in the parameters

of the projection, are discarded.

On the obtained structure, we filter the sleep logs relative to the last 48 h with the match

operator at Line 11.

At Line 12 we use the grouping operator γ to aggregate the quality of the sleep sessions

recorded in the same day and discarding the nodes day, month, and year.

At Line 13 we project within the sl data structure the pseudoID of the patient under

node patient _id. That value is used at Line 14 to join, with the lookup operator λ, the

obtained sleep logs with the previous values of temperatures (tmp). Lastly, we prepare the

data structure to be submitted for analysis. We do this at Line 15 by keeping the paths

quality and patient _id in bs and by moving the nested temperatures (temperatures.t ) under

the path temperatures—this is required by the interface of detectEncephalopathy, which we

invoke passing the resulting (bs) data structure.

THE TQUERY FORMALISATION

In this section, we report the formalisation of Tquery. Besides providing a general,

mathematical reference, the formalisation guides the implementation of our Jolie

framework, presented in ‘Implementation’. Tquery is inspired by MQuery (Botoeva et

Giallorenzo et al. (2022), PeerJ Comput. Sci., DOI 10.7717/peerj-cs.1037 11/40

al., 2018), a sound variant of the MongoDB Aggregation Framework (MongoDB Inc.,

2018a); the most popular query language for NoSQL data handling.

In our formal development, we favour a theory-to-practice strategy to avoid inconsistent

or counter-intuitive query behaviours, which is one of the significant drawbacks of the

MongoDB Aggregation Framework implementation (Botoeva et al., 2018). Moreover, we

consider the formalisation as a blueprint for implementors and thus we strive for a balance

between abstraction and technical involvement: (i) we adopt constructive semantics

definitions rather than declarative ones, since the former are more amenable to imperative

implementations, and (ii) we define our semantics on trees rather than on sets (as done

in Botoeva et al. (2018)), since the former is the data structure handled by the developers

and their users.

Data structures: tr ees and paths

We start by defining trees and the primitives on which we define the semantics of Tquery.

We denote trees with t . A tree contains two elements: (i) a root value that we denote

with b, which holds basic values (Booleans, integers, and strings) or the null value υ; (ii) a

set of pairs {k : a }, where each pair associates a key k to an array of trees a. Formally:

t ::= b {k

: a

}

a ::= [t

,...,t

]

We indicate with k(t ) the extraction of the array pointed by the label k in t: if k is present

in t we retrieve the related array, otherwise we return the null array α (different from the

empty array, instead denoted with [ ]). Formally:

k(b {k

: a

}

) =

(

a if (k : a) ∈ {k

: a

}

α otherwise

We assume the range of a given array a to run from the minimum index (one) to the

maximum, that corresponds to its cardinality, denoted with #a. We indicate the extraction

of the tree t at index i in array a with the index notation a[i], i.e., a[i] = t . In case a contains

an element at index i we retrieve it, otherwise, we retrieve the null tree, denoted with τ.

Formally:

a[i] =

(

if a = [t

,...,t

] ∧ 1 ≤ i ≤ n

τ otherwise

We define the array concatenation operator, denoted with ::, such that [t

,...,t

] =

] :: ... :: [t

]. Given two arrays a

and a

, the concatenation a

:: a

returns an array a

of size #a = #a

+ #a

where elements a[1],...,a[#a

] correspond point-wise to elements

[1],...,a

[#a

] and elements a[#a

+1],...,a[#a

+#a

] correspond point-wise to elements

[1],...,a

[#a

We define paths to express tree traversal, ranged over by p. Paths are concatenations of

expressions, indicated with e (which we omit to define since orthogonal to Tquery), closed

by the sequence termination ε. Formally:

p ::= e.

p | ε.

When possible, we omit to indicate sequence terminations ε in paths and we slightly abuse

the notation by indicating the components of paths like e.p as e.p to keep a lightweight

Giallorenzo et al. (2022), PeerJ Comput. Sci., DOI 10.7717/peerj-cs.1037 12/40

notation—this does not make the notation ambiguous since path concatenation is always

contextually distinct.

The application of a path p to a tree t, written [[p]]

, returns an array that contains

the sub-trees reached traversing t following p. To define [[p]]

, we introduce the notation

e ↓ k, read ‘‘e evaluates to k’’, and use it to indicate that the evaluation of the expression

e in a path p results in the label k. Path application [[p]]

neglects array indexes, i.e., for

p = e.p

, such that e ↓ k, we apply the sub-path p

to all trees in the array pointed by k

in t and concatenate all their results keeping their relative order—the resulting array can

concatenate null arrays α too, as a result of applying the path on some (sub)trees that do

not contain all nodes present in p.

[[p]]











[[p

]]

:: ... :: [[p

]]

if p = e.p

∧ e ↓k ∧k(t ) = [t

,...,t

]

[t ] if p = ε

α otherwise

We illustrate the path application with the example below, where t

= sl[1], i.e., it is the

first (and only) element in the sl data structure represented at Lines 7–16 of Listing 1.

From now on, in the examples, we adopt the formal representation of trees defined at the

beginning of the section.

[[M.m.D.d]]

= [ 27 {} , 28 { } , 29 { }, 30 {} ]

In the remainder, to contract empty and null arrays, we assume the following structural

equivalences when we perform array concatenations.

α ≡ α :: α α :: [] ≡ [] :: α ≡ [] :: [] ≡ [] α :: a ≡ a :: α ≡ a :: [] ≡ [] :: a ≡ a

Tquery operators

In this section, we present each Tquery operator: examples of its usage, its formal syntax,

and its semantics, with examples illustrating relevant steps. For reference, we report in

Fig. 1 the syntax of the Tquery operators: match (µ), unwind (ω), project (π), group (γ ),

and lookup (λ). In the syntax, a denotes arrays, b denotes primitive values, and p, q, and

r are paths. We define the parameters of the operators with four syntactic rules: ϕ for the

match, 5 and d for the project, and 0 for the group, explained in their relative sections.

The match operator

µ(a,ϕ) ϕ ::= true | ∃p | p = a | p

= p

| ¬ϕ | ϕ ∧ ϕ | ϕ ∨ ϕ

The purpose of the match operator is to select trees in an array a according to a criterion

ϕ, which can be (from left to right): (i) the Boolean truth, (ii) the existence of a path p in

t, (iii) the equality between the application of a path p on t and a given array a, (iv) the

equality between the applications of two paths p

and p

on t, and the logic connectives

(v) negation, (vi) conjunction, and (vii) disjunction.

Example Here and in the following sections, we draw our examples from Listing 2. There,

we see the match operator used twice: the first at Line 3 and the second at Line 11. Here,

we focus on the example at Line 3. We comment the execution of Line 11 in ‘The group

operator’, since we use it to filter out the unnecessary values from the sl data structure

before the application of the group.

Giallorenzo et al. (2022), PeerJ Comput. Sci., DOI 10.7717/peerj-cs.1037 13/40

operator ::= µ(a,ϕ) | ω(a, p) | π(a,Π) | γ(a, Γ, Γ

′

) | λ (a, q, a

′

,r, p)

ϕ ::= true | ∃p | p = a | p

= p

| ¬ϕ | ϕ ' ϕ | ϕ ( ϕ

Π ::= p | d ð p | p, Π | d ð p,Π

d ::= b | p |

,... , d

] | ϕ | ϕ?d

: d

Γ ::= p | p ð p

′

| p, Γ | p ð p

′

,Γ

Figure 1. Syntax of Tquery.

In the remainder, to contract empty and null arrays, we assume the following structural equivalences

when we perform array concatenations.

α ≡ α :: α α :: [] ≡ [] :: α ≡ [] :: [] ≡ [] α :: a ≡ a :: α ≡ a :: [] ≡ [] :: a ≡ a

4.2 Tquery operators429

In this section, we present each Tquery operator: examples of its usage, its formal syntax, and its

430

semantics, with examples illustrating relevant steps. For reference, we report in Figure 1 the syntax of the

431

Tquery operators: match (

), unwind (

), project (

), group (

), and lookup (

). In the syntax,

denotes

432

arrays,

denotes primitive values, and

, and

are paths. We deﬁne the parameters of the operators

433

with four syntactic rules:

for the match,

and

for the project, and

for the group, explained in their

434

relative sections.435

4.2.1 The match operator436

µ(a,ϕ) ϕ ::= true | ∃p | p = a | p

= p

| ¬ϕ | ϕ ' ϕ | ϕ ( ϕ

The purpose of the match operator is to select trees in an array

according to a criterion

, which can be

437

(from left to right): i) the Boolean truth, ii) the existence of a path

, iii) the equality between the

438

application of a path

and a given array

, iv) the equality between the applications of two paths

439

and p

on t, and the logic connectives v) negation, vi) conjunction, and vii) disjunction.440

Example Here and in the following sections, we draw our examples from

Listing 2. There, we see the441

match operator used twice: the ﬁrst at Line 3 and the second at Line 11. Here, we focus on the example at

442

Line 3. We comment the execution of Line 11 in Section 4.2.4, since we use it to ﬁlter out the unnecessary

443

values from the sl data structure before the application of the group.444

At Line 3, we use a match to ﬁlter

tmp

from those trees that do not correspond to the time range of

445

interest. For convenience, we report Line

3 of Listing 2 in the snippet below.446

447

3 tmp← µ ( tmp , (date=2020112 8 ( date= 2 0201129 ( date= 2 0 201130) )448

449

The execution takes as input the data structure

tmp

presented in Listing 1 and assigns to it the resulting

450

data structure:451

452

[ υ { date : [ 2 0201128 { } ], t : [ 36 {} ] , hr : [ 66 { } ] } ,453

υ { date : [ 2020112 9 { } ], t : [ 36 {} ] , hr : [ 65 { } ] } ,454

υ { date : [ 2020113 0 { } ], t : [ 37 {} ] , hr : [ 67 { } ] } ]455

456

Semantics When applied to an array

, the match operator returns a new array in the shape of

but

including only its elements that satisfy

. If no element matches the criterion (and also in the case that

a = α), the operator returns an empty array [].

µ(α,ϕ) = [] µ([t] :: a, ϕ) =











[t] :: µ(a, ϕ) if t |= ϕ

µ(a,ϕ) if #a > 0

[] otherwise

10/30

PeerJ Comput. Sci. reviewing PDF | (CS-2022:04:72509:1:0:NEW 17 Jun 2022)

Manuscript to be reviewed

Computer Science

Figure 1 Syntax of Tquery.

Full-size DOI: 10.7717/peerjcs.1037/fig-1

At Line 3, we use a match to filter tmp from those trees that do not correspond to the

time range of interest. For convenience, we report Line 3 of Listing 2 in the snippet below.

3 tmp← µ( tmp , (date = 20201128 ∨ date =20201 1 2 9 ∨ date = 2 0 2 0 1130) )

The execution takes as input the data structure tmp presented in Listing 1 and assigns to

it the resulting data structure:

[ υ { d ate :[ 2 0 2 01128 { } ], t :[ 36 {} ] , hr :[ 66 {} ] },

υ { date : [ 2020112 9 { } ], t :[ 36 {} ] , hr : [ 65 {} ] },

υ { date : [ 2020113 0 { } ], t :[ 37 {} ] , hr : [ 67 {} ] } ]

Semantics When applied to an array a, the match operator returns a new array in the shape

of a but including only its elements that satisfy ϕ. If no element matches the criterion (and

also in the case that a = α), the operator returns an empty array [ ].

µ(α,ϕ) = [] µ( [t ] :: a,ϕ) =











[t ] :: µ(a,ϕ) if t |H ϕ

µ(a,ϕ) if #a > 0

[] otherwise

The semantics of t |H ϕ is defined by the Boolean expressions below.

t |H ϕ holds iff











ϕ = true

ϕ = (∃p) ∧ [[p]]

6= α

ϕ = (p = a) ∧ [[p]]

= a

ϕ = (p

= p

) ∧ [[p

]]

= [[p

]]

ϕ = (¬ϕ

) ∧ t 6|H ϕ

ϕ = (ϕ

∧ ϕ

) ∧ (t |H ϕ

∧ t |H ϕ

)

ϕ = (ϕ

∨ ϕ

) ∧ (t |H ϕ

∨ t |H ϕ

)

Example: semantics At Line 3 of Listing 2, the match evaluates all trees inside tmp and

verifies which one of the sub-conditions hold for each element of tmp. In the case of tmp[1],

the criterion is not satisfied and thus the value is discarded. Next, tmp[2] satisfies the first

criterion date = 20201128, tmp[3] satisfies the second criterion date = 20201129, and tmp[4]

satisfies the third criterion date = 20201130.

The unwind operator

ω(a,p)

Giallorenzo et al. (2022), PeerJ Comput. Sci., DOI 10.7717/peerj-cs.1037 14/40

The purpose of the unwind operator is to unfold the elements of an array a under a

given path p.

Example We exemplify the usage of unwind reporting Line 9 of Listing 2 in the snippet

below and later showing the result of its application.

9 sl← ω( sl , M.D.L )

The unwind operator takes as input the sleep logs sl (as retrieved from the invocation

of the getSleepPatterns operation at Line 8, and represented at Lines 7–16 of Listing 1). In

the snippet, we update the content of sl to contain the new data structure, shown below.

[ υ { y: [ 2020 { } ] , M:[ υ { m :[ 11 {} ] , D :[ υ { d :[ 27 {} ] ,

L: [ υ { s:[ ' 23 :33 '{ } ],e : [ ' 07:0 4 '{} ] , q : [ ' poo r '{} ] }] } ] }] } ,

υ { y:[ 202 0 { } ], M : [ υ { m:[ 11 { } ], D : [ υ { d: [ 28 { } ],

L: [ υ { s:[ ' 21 :13 '{ } ],e : [ ' 09:3 4 '{} ] , q : [ ' goo d '{} ] }] } ] }] } ,

υ { y:[ 202 0 { } ], M : [ υ { m:[ 11 { } ], D : [ υ { d: [ 29 { } ],

L: [ υ { s:[ ' 21 :01 '{ } ],e : [ ' 03:1 2 '{} ] , q : [ ' goo d '{} ] }] } ] }] } ,

... ]

Semantics To define the semantics of the unwind operator ω, we introduce an auxiliary

operator, called unwind expansion operator and we indicate it with ueo(t ,a,k) (read

‘‘unwind t on a under k’’). Informally, ueo(t ,a,k) returns an array of trees with cardinality

#a where each element has the shape of t except that label k points to the corresponding

index-wise element in a.

Formally, given a tree t , an array a, and a key k:

ueo(t,a,k) =













(

: a

}

\ {k : k(t) }

)

∪ {k : [t

] }



] :: ueo(t ,a

,k) if a = [t

] :: a

∧t = b {k

: a

}

[] otherwise

Then, the formal definition of ω(a,p) is

ω(a,p) =











ueo(t,ω([[k.ε]]

),k) :: ω(a

,p) if p = e.p

∧ e ↓k ∧a = [t ] :: a

a if p = ε

[] otherwise

Essentially, the semantics of the unwind operator follows two inductive directions: one

on arrays and the other on paths. Hence, to simplify the explanation of the semantics, we

describe it following a spatial interpretation of the two directions: the induction on arrays

is the ‘‘breadth’’ of the expansion while the induction on paths represents its ‘‘depth’’.

The first part of the breadth expansion corresponds to the induction over the array a,

which results in the concatenation of the inductive application of the depth expansion

of p over each element t of a. In turn, the depth expansion consists of a nested depth

expansion with a breadth one. The depth expansion is represented by ω([[k.ε]]

), which

corresponds to the application of the unwind operator with path p

—the suffix of k in

p—and on the array of subtrees found in t under the current path fragment k. The breadth

expansion (which complements the breadth expansion on the array a) uses the unwind

expansion operator (ueo) to apply the result of the nested depth expansion on all elements

found under k in t.

Example: semantics We now report excerpts of the execution of the unwind operator at

Line 9 of Listing 2 to exemplify both the unfolding of the breadth and depth expansions.

Giallorenzo et al. (2022), PeerJ Comput. Sci., DOI 10.7717/peerj-cs.1037 15/40

We remind that sl has the shape reported in Line 7 in Listing 1 and that the application

at Line 9 of Listing 2 ‘‘unwinds’’ the sl data structure with path M .D.L.

The first expansion we perform is the breadth expansion over the array sl. Since sl

just contains one tree, i.e., that for sleep logs of 2020, we just have one application of the

ueo operator (the empty array [] at the right of the concatenation operator :: results

from the ‘‘otherwise’’ branch of the definition of the unwind and fromsl being structurally

equivalent to sl[1] :: []).

ueo(sl[1],ω([[M.ε]]

sl[1]

,D.L),M) ::[]

Then, we show the ‘‘depth’’ part of the expansion, by focusing on the terminal part of

the application of the ueo operator. Specifically, we concentrate on the tree corresponding

to the sleep logs of day 2020-11-29, found at Line 11 of Listing 1 and aliased with the tree

. Formally, the expansion corresponds to the application ueo(t

,[[L.ε]]

,L) of the

terminal node L in path M.D.L.

ueo (t

,[[L.ε]]

, L) ⇒

[(ν{ d : [ 29 {}], L : [ ... ] }\ν{L : [ ... ] } )∪ν{L: [ν { s : [ ' 2 1: 01 '{ } ] , e :[ ' 03:12 ' { }],q : [ ' good '{}

]}]} ] ::

[(ν{ d : [ 29 {}], L : [ ... ] }\ν{L : [ ... ] } )∪ν{L: [ν { s : [ ' 0 3: 36 '{ } ] , e :[ ' 09:58 ' { }],q : [ ' good '{}

]}]} ]

Above, for each element of the array pointed by L, we create a new structure where

we replace the original array associated with the key L with a new array containing only

one element. For instance, the first element of the result takes the original structure found

under D ( [ν {d : [29{}],L : [... ] } ]) and updates it to contain only the element υ { s: [

'21:01' { } ], e: [ '03:12' { } ], q: [ ’good’ { } ] } associated to the node L.

The project operator

π(a,5) 5 ::= p | d i p | p,5 | d i p,5 d ::= b | p | [d

,...,d

] | ϕ | ϕ?d

: d

The purpose of the project operator is to modify the trees in an array a by projecting nodes,

renaming node labels, or introducing new nodes, as described in the sequence of elements

5, which are either a path p or an injection ( i ) of a value definition d into a path.

A value definition d can be (in the grammar, from left to right): (i) a value, (ii) a path,

(iii) an array of value definitions, (iv) a criterion (ϕ) (cf. ‘The match operator’’) or (v) a

ternary expression on a criterion and two value definitions.

Example As done for the other operators, we draw our examples from Listing 2, where we

have four usages of the project operator, the first at Line 5, the second at Line 10, the third

at Line 13, and the fourth at Line 15. Here, we focus on the second example, at Line 10,

reported in the snippet below. We comment on the others when exemplifying the lookup

operator in ‘The lookup operator’.

10 sl← π ( sl , (y i year , M.m i month, M.D.d i day , M.D.L.q i quality ) )

The projection at Line 10 takes the sl data structure resulting from the application of

the unwind at Line 9 and performs a sequence of renaming over all tress within sl. For

each tree, we perform the rename of the node y in year by moving the content of path y

Giallorenzo et al. (2022), PeerJ Comput. Sci., DOI 10.7717/peerj-cs.1037 16/40

into the node corresponding to path year, represented by the fragment y i year. Similarly,

we move the content of M.m under month, of M .D.d under day, and of M.D.L.q under

quality. The result of the projection is the following flattened structure:

[ υ { y ear : [ 2020 {} ], month : [ 11 { } ], day : [ 27 { } ], qu al ity : [ ' poo r '{} ] } ,

υ { year :[ 2020 { } ] , month :[ 11 { } ] , day : [ 28 { } ] , qua li ty : [ ' good ' {} ] },

υ { year :[ 2020 { } ] , month :[ 11 { } ] , day : [ 29 { } ] , qua li ty : [ ' good ' {} ] },

... ]

Semantics We start by defining the auxiliary operators we use in the definition of the

project. Auxiliary operators π(a,p) and π (t ,p) formalise the application of a branch

selection over a path p respectively over an array and a tree. Then, we define the auxiliary

operator eval(d,t), which returns the array resulting from the evaluation of a value

definition d over a tree t . Finally, we report the projection of an injection of a value

definition d into a path p over a tree t , i.e., π(t,d i p).

The projection π (a,p) for a path p over an array a results in an array whose elements

are the projection for p of the elements of a:

π(a,p) = π( [t

,...,t

],p) = [π(t

,p),...,π(t

,p) ]

The projection π(t ,p) for a path p over a tree t implements the actual semantics of

branch selection, where, given a path e.p

with e ↓ k, we remove all the branches k

t = b {k

: a

}

but k (if k ∈ {k

}

) and continue to apply the projection for the continuation

over the (array of) sub-trees under k in t (i.e., [[k.ε]]

). Formally:

π(t,p) =











υ {k : π([[k. ]]

) } if [[p]]

6= α ∧ p = e.p

∧ e ↓k

t if p = ε

τ otherwise

The operator eval(d,t ) evaluates the value definition d over the tree t and returns an

array containing the result of the evaluation. Formally:

eval(d,t ) =











[d {} ] if d ∈ V

[[d ]]

if d ∈ P

eval(d,t ) :: eval(d

,t ) if d = [d ] :: d

[t |H ϕ {} ] if d = ϕ

eval(d

,t ) if d = ϕ?d

: d

∧ t |H ϕ

eval(d

,t ) if d = ϕ?d

: d

∧ t 6|H ϕ

α otherwise

The projection π (t ,d i p) of the injection of the evaluation of a value definition d on a

tree t into a path p results in a new tree where we find the evaluation of d on t under p.

π(t,d i p) =











υ {k : [π (t ,d i p

) ] } if p = e.p

∧ e ↓k ∧eval(d,t ) 6= α

υ {k : eval(d,t ) } if p = e.ε ∧ e ↓ k ∧ eval(d,t ) 6= α

τ otherwise

Before formalising the projection, we report the auxiliary operator ⊕ to merge arrays

and trees—we use the operator to merge the result of sequences of projections in the

definition of π(t,5).

( [t ] :: a) ⊕ ( [t

] :: a

) = [t ⊕ t

] :: a ⊕ a

t ⊕ τ = t a⊕ [] = [] ⊕ a = a⊕ α = α ⊕ a = a

Giallorenzo et al. (2022), PeerJ Comput. Sci., DOI 10.7717/peerj-cs.1037 17/40

t = b {k

: a

}

i∈I

∧ t

= b {k

: a

}

j∈J

t ⊕ t

= b {k

: k

(t) ⊕ k

) }

h∈I∪J

b 6= b

b {k

: a

}

⊕ b

: a

}

= τ

To conclude, we first report the application of the projection to a tree t , π(t,5), which

merges the results of projections in 5 over t into a single tree. Second, we report the

application of the projection to an array a, π(a,5), which corresponds to the application

of the projection to all elements of a. Respectively, we formally write:

π(t,5) =











π(t,p) ⊕ (π (t ,5

)) if 5 = p,5

π(t,d i p) ⊕ (π (t ,5

)) if 5 = d i p,5

π(t,p) if 5 = p

π(t,d i p) if 5 = d i p

and

π(a,5) = π( [t

,...,t

],5) = [π(t

,5),...,π(t

,5) ] π( [],5) = π (α,5) = []

Example: semantics We report the execution of the project at Line 10 of Listing 2. We

take sl as returned after the application of the unwind operator described in ‘The unwind

operator’. For brevity, we represent the sl data structure as the concatenation of its

elements, i.e., sl = sl[1] :: sl[2] :: ···.

π ( sl[1] ::sl[2] :: ···, (y i year, M.m i month, M .D.d i day, M.D.L.q i quality)) ⇒

[ π (sl[1],(y i year, M .m i month, M .D.d i day, M .D.L.q i quality)),

π(sl[2],(y i year, M .m i month, M.D.d i day, M .D.L.q i quality)), ... ]

We continue showing the projection of the first element in a, sl[1] (the projection on

the other elements follows the same structure)

π(sl[1],(y i year, M .m i month, M.D.d i day, M .D.L.q i quality)) ⇒

π(sl[1],y i year) ⊕ π(sl[1],M.m i month) ⊕ π (sl[1],M.D.d i day) ⊕ π (sl[1],M.D.L.q i quality)

Finally, we show the unfolding of the first two projections from the left, above, i.e., those

for y i year and for M .m i month, and their merge ⊕ (the remaining ones unfold similarly).

π(sl[1],y i year)⊕ π (sl[1],M .m i month)

⇒ υ { yea r : π (sl[1],y) } ⊕ υ { month : π(sl[1],M.m) }

⇒ υ { yea r : eval(y,sl[1]) } ⊕ υ { month : eval(M .m,sl[1]) }

⇒ υ { yea r : [[y ]]

sl[1]

} ⊕ υ { month : [[M.m]]

sl[1]

}

⇒ υ { yea r : [ 20 20 { } ] } ⊕ υ { month : [ 11 {}] }

⇒ υ { yea r : [ 20 20 { } ], m on th : [ 11 {}] }

The group operator

γ (a,0,0

) 0 ::= p | p i p

| p,0 | p i p

The purpose of the group operator is to group the trees in an array a according to

a specification 0

and to aggregate the values of the grouped trees according to the

specification 0. Both 0 and 0

, respectively the aggregation and the grouping set, are

sequences of elements of the form p i p

where p is a path in the input trees, and p

a path

in the output trees.

Giallorenzo et al. (2022), PeerJ Comput. Sci., DOI 10.7717/peerj-cs.1037 18/40

Note that 0 includes both fragments of the shape p and p i p

. Here, the former is

syntactic sugar for the latter, where both paths are the same. Therefore, we assume to

apply the semantics of the group operator only with the de-sugared form γ (a,0,0

) =

γ (a,exp(0),exp(0

)), where

exp(0

) = exp(0

),exp(0

) exp(p) = p i p exp(q i p) = q i p

Example Drawing from Listing 2, we have two applications of the group operator, one at

Line 4 and the second at Line 12. Since the two applications are similar, we just focus on

the latter (reported below), leaving the comment on the second to ‘The lookup operator’.

12 sl← γ ( sl , (quality ), () )

As stated above, the aggregation set expands from quality to the de-sugared form

quality i quality.

The group operator applies on the data structure in sl which, at Line 11, we filtered

with the match operator to only contain values corresponding to the dates 2020-11-29 and

2020-11-30. The new data structure, copied into sl and reported below, is essentially the

aggregation under the node quality of the filtered sleep recordings.

[ υ { qual i ty : [ ' goo d '{}, ' g ood '{ } , ' poor ' { } , ' goo d '{} ] } ]

To make for a more comprehensive illustration, in this section we consider an alternative

version of the example above, where we want to use the group operator to group the values

by day, month, and year and aggregate the values of the sleep quality. Concretely, we do

this by updating the command found at Line 12 with the sequence of paths replacing the

third parameter, which in the original we left empty.

sl← γ ( sl , (quality ), (day , month, year ) )

As stated, the paths quality, day, month, and year respectively expand to quality i quality,

day i day, month i month, and year i year.

The main detail we want to notice here is that, by grouping the values by year, month,

and day, we only aggregate logs relative to the same day.

[ υ { y e a r : [ 2020 { } ] , month : [ 11 { } ] , day : [ 29 { } ] , q u a l i t y : [ ' good ' { } , ' good ' { } ] } ,

υ { y e a r : [ 2020 { } ] , month : [ 11 { } ] , day : [ 30 { } ] , q u a l i t y : [ ' po or ' { } , ' good ' { } ] }

]

Semantics We start by reminding the shape of the de-sugared syntax of the group operator.

γ (a,0,0

) = γ (a,exp(0),exp(0

)) = γ (a , q

,...,q

| {z }

aggregation set A

, s

,...,s

| {z }

grouping set G

)

Intuitively, the group operator performs the following actions:

(a) it groups together those trees in a that (1) have the maximal number of existing paths

from the grouping set s

,...,s

and (2) whose values under those paths coincide;

(b) it projects the values in the grouped trees from s

,...,s

to the corresponding paths

,...,r

;

Giallorenzo et al. (2022), PeerJ Comput. Sci., DOI 10.7717/peerj-cs.1037 19/40

,...,q

from

the aggregation set;

(d) it projects the aggregated values from q

,...,q

into the corresponding paths p

,...,p

Formally, let S = {s

,...,s

} be the set of left elements in the injections of the sequence

in the grouping set and let 6 be the power-set 2

of paths in S so that

6 =

{

∅,{s

},{s

},...,{s

},{s

},...,{s

,...,s

}

= {σ

,...,σ

}

We define the auxiliary operator exists which takes S and an element σ ∈ 6 and builds

the existence-match-query formula of the paths in S w.r.t. the combination identified by σ .

exists(σ ,S) =











true if S = ∅

∃s∧ exists(σ ,S\ {s}) let s ∈ S and s ∈ σ

¬∃s∧ exists(σ ,S\ {s}) let s ∈ S and s 6∈ σ

We use the exists operator to perform part 1) of Item (a), i.e., grouping those trees in

a so that the trees in the same group have the same set of existing and non-existing paths

from s

,...,s

. The part operator (presented below) performs part 2) of Item (a), which is

the partition of the trees grouped by the exists operator so that the values in their existing

paths in s

,...,s

coincide.

We now define the semantics of the group operator and then present the semantics of

the part operator. In the remainder, to make the definitions more intuitive, we alias the

aggregation set with A and the grouping set with G. Let, k = |6|, we write

γ (a,A,G) = part(µ(a,exists(σ

,S)),σ

,A,G) :: ··· :: part(µ(a,exists(σ

,S)),σ

,A,G)

As mentioned, the part operator finds the elements of a which should be grouped

together according to G (among those selected through σ ). In the definition, we delegate

the actual grouping to the other auxiliary operator group, which (as hinted in Item (b))

projects the partitioned values from S into the corresponding destination path r

,... in

G. The group operator also performs the aggregation of the values found in q

,... (Item

(c)) and it projects them under the corresponding destination path p

,... (Item (d)).

In the semantics of the part operator, we assume to extend the set difference \ to arrays,

so that a\ a

returns a copy of a without the elements found in a

(preserving their relative

order). We also assume to have a variant of the match operator µ

(a,ϕ) that, instead of

returning the array of trees in a that match the criterion ϕ, it returns the array of their

indexes in a.

part(a,σ ,A,G) =











a if a = []

group(a,σ ,A,G) if σ = ∅

otherwise, let σ = {s

,...,s

group( [a[j],...,a[k] ],σ ,A,G)

:: part( [a[f ],...,a[g] ],σ ,A,G) µ

(a,

j=1

= [[s

]]

a[1]

) = [j,...,k ],

[f ,...,g ] = [1,...,#a ]\ [j,...,k ]

Finally, we report below the definition of the group operator. There, the last case is where

we aggregate the values found in the array a following the paths in A, and we combine

them with the grouped values from G by using the project operator. The aggregation of

the values in a is done by invoking the group operator on the second case. The second

case applies when σ = ∅ (i.e., when no path S is selected for grouping). The result of the

Giallorenzo et al. (2022), PeerJ Comput. Sci., DOI 10.7717/peerj-cs.1037 20/40

application of the second case is an array containing one tree that combines the values

of the array a following the paths in A. To aggregate the values, we use the auxiliary tree

variant of the project operator (π (t ,5), cf. ‘The project operator’) to project each value

for a given path q into its corresponding path p in A.

group(a,σ ,A,G) =











a if a = []

[π(τ ,η

,...,η

| {z }

aggregation

) ] if σ = ∅, let A = q

,...,q

= π (a,q

), j ∈ [1,n]

π(a

,[[s

]]

a[1]

,...,[[s

]]

a[1]

| {z }

grouping

) otherwise, let a

= group(a,∅,A,G)

G = s

,...,s

σ = {s

,...,s

},1 ≤ i ≤ j ≤ m

Example: semantics To illustrate the semantics of the group operator, we consider the

alternative version of the code shown at Line 12 (and presented as a second example at the

beginning of this section), where we want to aggregate for quality but we also want to keep

those values grouped by year, month, and day.

sl← γ ( sl , (quality ), (day , month, year ) )

In the semantics, the first thing we do is the de-sugaring of paths—namely quality, day,

month, and year, which respectively expand to quality i quality, day i day, month i month,

and year i year—and then we apply the de-sugared group operator on sl (which, we

remind, contains only values corresponding to the dates 2020-11-29 and 2020-11-30,

represented by the trees t

,... below).

part(a,σ , A ,G ) =











a if a =

[]

group(a,σ,A ,G ) if σ = ∅

group(

[a[ j],. . .,a[k]], σ,A , G )

:: part(

[a[ f ],. . .,a[g]],σ , A ,G )

otherwise, let σ = {s

,... , s

(a,



j=1

= [[ s

]]

a[1]

) =

[ j,. . .,k],

[ f ,.. . , g] = [1, . . .,#a] \ [ j,. ..,k]

Finally, we report below the deﬁnition of the

group

operator. There, the last case is where we aggregate

626

the values found in the array a following the paths in A , and we combine them with the grouped values627

from

by using the project operator. The aggregation of the values in

is done by invoking the

group628

operator on the second case. The second case applies when

σ = ∅

(i.e., when no path

is selected for

629

grouping). The result of the application of the second case is an array containing one tree that combines630

the values of the array

following the paths in

. To aggregate the values, we use the auxiliary tree

631

variant of the project operator (

π(t,Π)

, cf. Section 4.2.3) to project each value for a given path

into its

632

corresponding path p in A .633

group(a,

σ,A , G ) =











a if a =

[]

[

π(τ,η

ðp

,... ,

ðp



 

aggregation

)

]

if σ = ∅, let A = q

ðp

,... , q

ðp

π(a, q

), j ∈ [1, n]

π(a

′

,[[ s

]]

a[1]

ðr

,... , [[s

]]

a[1]

ðr



 

grouping

)

otherwise, let a

′

= group(a,∅, A ,G )

G = s

ðr

,... , s

ðr

σ = {s

,... , s

},1 f i f j f m

Example: semantics To illustrate the semantics of the group operator, we consider the alternative

634

version of the code shown at Line 12 (and presented as a second example at the beginning of this section),

635

where we want to aggregate for

quality

but we also want to keep those values grouped by

year

month

636

and day

.637

638

sl← γ ( sl , (quality), (day, month, year) )639

640

In the semantics, the ﬁrst thing we do is the de-sugaring of paths—namely

quality

day

month

, and

641

year

, which respectively expand to

quality ð quality

day ð day

month ð month

, and

year ð year

—and

642

then we apply the de-sugared group operator on

(which, we remind, contains only values corresponding

643

to the dates 2020-11-29 and 2020-11-30, represented by the trees t

,... below).644

γ(sl, (quality),(day,month,year))

⇒

γ(sl, (qualityðquality),(dayðday,monthðmonth,yearðyear))

let S = {day

,month,year},

A = quality

ðquality, and

G = day

ðday,monthðmonth,yearðyear

⇒ part(µ(sl,exists(∅,S)),∅,A ,G ) :: ··· :: p art(µ(sl,exists(S,S)), S, A , G )

⇒ group([],∅,A ,G ) :: ··· :: group([t

],S,A ,G ) :: group([t

],S,A ,G )

⇒

[] :: · · ·

π([υ { quality:['good'{},'good'{}] } ],([29{}] ð day,[11{}] ð month,[2020{}] ð year))

π([υ { quality:['poor'{},'good'{}] } ],([30{}] ð day,[11{}] ð month,[2020{}] ð year))

⇒

[ υ {year:[2020{}],month:[11{}],day:[29{}],quality:['good'{},'good'{}]} ,

υ {year:[2020{}],month:[11{}],day:[30{}],quality:['poor'{},'good'{}]} ]

645

16/

PeerJ Comput. Sci. reviewing PDF | (CS-2022:04:72509:1:0:NEW 17 Jun 2022)

Manuscript to be reviewed

Computer Science

The lookup operator

λ(a,q,a

,r,p)

The purpose of the lookup operator is to join the trees in a source array a with the trees

in an adjunct array a

. For those values obtained by applying the path q on a, the lookup

operator pairs them with the equivalent values obtained by applying r on the adjunct array

and it projects the latter under path p in the paired trees of a.

Example Before commenting on the application of the lookup in Listing 2, we describe the

results of the group at Line 4 and of the two projections, respectively at Line 5 and Line 13.

At Line 4, we aggregate the temperatures in the tmp data structure, which results into

Giallorenzo et al. (2022), PeerJ Comput. Sci., DOI 10.7717/peerj-cs.1037 21/40

[ υ { t:[ 36 { } , 36 { } , 37 {} ] } ]

The projection at Line 5 performs two actions over the tmp data structure. First, it keeps

only the node t (holding the temperatures filtered for the days of interest). Second, it

projects into the filtered data structure the pseudo-identifier (pseudoID) under the node

patient_id.

[ υ { t:[ 36 { } , 36 { } , 37 {} ] , p a t i e nt_id : [ ' id_xx x '{ } ] } ]

The projection at Line 13, similar to the one above, keeps only the node quality (holding

the quality of the sleep for the days of interest) and it projects the pseudoID under the node

patient_id.

[ υ { qual i ty : [ ' goo d '{}, ' g ood '{ } , ' poor ' { } , ' goo d '{} ] ,

patien t _ i d : [ ' id_ xxx '{} ] } ]

We can now comment on the lookup at Line 14, which we report below for convenience.

14 bs← λ( sl , patient _id , tmp , patient_id , temperatures )

The instruction joins the data structures tmp and sl by pairing the values under the path

patient_id (this is a special case where the left and right paths of the join coincide, i.e., the

path patient_id). The last path in the application, i.e., temperatures, indicates where the

values from the right data structure (tmp) should be projected in the paired values of the

left one (sl).

At Line 14, we store the result of the application of the lookup into a new variable bs

(standing for bio-signals).

υ { q u ality :[ ' good '{} , ' good ' { } , ' poor '{}, ' g ood '{ } ],

temperatures : [ υ { t: [ 36 { }, 36 {} , 37 { } ],

patien t _ i d : [ ' id_x xx '{} ] } ] ,

patien t _ i d : [ ' id_x xx '{} ] }

For completeness, we report the result of the last step of Listing 2, at Line 15, where

we apply the project operator to reshape the data structure for the invocation of the

detectEncephalopathy functionality at Line 16.

υ { q u ality :[ ' good '{} , ' good ' { } , ' poor '{}, ' g ood '{ } ],

temperatures : [ 36 { } , 36 { }, 37 {} ] ,

patien t _ i d : [ ' id_x xx '{} ] }

Semantics In the semantics of the lookup, for each element a[i] (1 ≤ i ≤ #a), we use the tree

version of the project operator (π(t,5), cf. ‘The project operator’) to merge the element

a[i] with the paired values from a

under r. Since, by its definition, π (t ,5) corresponds to

the merging (⊕) of the single applications of each component in the sequence 5, we use

this to merge the source tree a[i] with the paired elements in a

. Hence, for each element

a[i], we define 5

as the sequence ε, µ(a

,ϕ

) i p. The projection for the first component

(ε) returns the original tree (a[i]). The projection for the second component (µ(a

,ϕ

) i p)

injects the result of the match µ(a

,ϕ

) into the path p, where the criterion ϕ

, equal to

r = [[q]]

a[i]

, selects those values in a

that under r coincide with the array found under q in

a[i].

Giallorenzo et al. (2022), PeerJ Comput. Sci., DOI 10.7717/peerj-cs.1037 22/40

These specify the network, transport, and

application protocols, e.g., HTTP/TCP/IP.

Note that when for some i we have q not present in a[i] (i.e., [[q]]

a[i]

= α) the lookup

operator joins a[i] with those trees in a

where r does not exist (i.e., µ(a

,r = α)).

λ(a,q,a

,r,p) = [π (a[1],5

) ] :: ··· :: [π(a[#a],5

) ] where











1 ≤ i ≤ #a

= ε,µ(a

,ϕ

) i p

= (r = [[q]]

a[i]

)

Example: semantics Below, we report the unfolding of the execution of the lookup at Line

14. Since we have one value in sl, we do not perform a concatenation of arrays but we just

apply the projection for sl[1]. In the three reductions below, first, we retrieve the content of

[[patient _id ]]

sl[1]

, then, we execute the match (which essentially returns the whole content

of the tmp variable), and, finally, we merge sl[1] (obtained by the projection under ε) with

the result of the match projected under path temperatures.

[π(sl[1],(ε,µ(tmp,patient _id = [[patient_id ]]

sl[1]

) i temperatures))]

⇒ [π (sl[1],(ε,µ(tmp,patient _id = [ ' i d_ xxx ' {}] ) i temperatures))]

⇒ [π (sl[1],(ε,[υ {t: [ 36 { } , 36 {}, 37 { } ] , pat ien t_i d : [ ' i d_xxx '{ } ] }] i temperatures))]

⇒ [υ { q ua lit y :[ ' good '{ } , ' goo d '{} , ' poor ' {}, ' go od ' {}],

pa t ie n t_ i d :[ ' i d_ xxx ' {}]} ⊕ υ { te m per atu r es : [υ { t : [ 36 {}, 36 { } , 37 {}],

pa t ie n t_ i d :[ ' i d_ xxx ' {}]}] } ]

IMPLEMENTATION

We now present Jolie/Tquery, our implementation of Tquery as a Jolie microservice.

Specifically, we chose to release Jolie/Tquery as a library that users can include and invoke

locally in their Jolie projects—as an npm package (https://www.npmjs.com/package/@jolie/

tquery.). However, thanks to Jolie’s module system, users can also expose Jolie/Tquery as an

independent service, e.g., as a RESTful service (Montesi, 2016) as well as a publish/subscribe

MQTT worker (Gabbrielli et al., 2018) (as briefly detailed in ‘The implementation of

Jolie/Tquery’).

In this section, first, we describe the main components of Jolie/Tquery, specified through

the abstractions provided by the Jolie language (which follow the typical partition of

microservice components (Giallorenzo et al., 2021)), namely: its Application Programming

Interfaces (API), its access points, and its logic/behaviour. In particular, APIs and access

points

describe how users interact with Jolie/Tquery, while the behaviour implements the

semantics of Tquery (cf. ‘The Tquery Formalisation’).

Then, we slightly extend the API and behaviour of Jolie/Tquery to support query

pipelines, i.e., multi-stage queries where (a) the first stage uses the data provided as input,

(b) each other stage transforms the data from the proceeding stage, and (c) the last stage

returns its output back to the invoker. We have two main reasons for extending Jolie/Tquery

with pipelines: (i) for efficiency, since it removes the overhead of data transmission between

sequential stages (as, e.g., in Listing 2 at Lines 3–5 and Lines 9–15); (ii) for familiarity with

the MongoDB Aggregation Framework (MongoDB Inc., 2022), where users express queries

as multi-stage transformations.

Giallorenzo et al. (2022), PeerJ Comput. Sci., DOI 10.7717/peerj-cs.1037 23/40

Available at https://github.com/jolie/tquery/

blob/master/main.ol.

A possible alternative, here, is using

asynchronous oneWays and either choose

a pull or push semantics to retrieve the

results of the queries. We did not pursue

this direction, since this modality would

sensibly diverge from that of Tquery.

Finally, we show the implementation of the example from ‘Overview and Running

Example’ in Jolie/Tquery, both using the original sequence of operators (cf. ‘Overview and

Running Example’) and as a combination of multi-stage pipelines.

The implementation of Jolie/Tquery

We start from the API of Jolie/Tquery and then present how Jolie allows us to provide the

microservice as a library and to also have an efficient implementation of its engine.

The Jolie/Tquery API Simplifying (Giallorenzo et al., 2021; Montesi, Guidi & Zavattaro,

2014), in Jolie, the API of a microservice corresponds to an interface, which is a named

collection of resources, called operations, each defined by a name, an interaction modality—

i.e., asynchronous invocations or synchronous request responses (W3c, 2001)—and

schemas of their expected inbound and outbound data, called types. Thus, in Fig. 2,

we report the API of Jolie/Tquery expressed as a Jolie interface, with its associated types.

The code in Fig. 2 is a fragment of the main.ol

executable Jolie file from Jolie/Tquery. In

Fig. 2, we stylise the code omitting void root types (described in the following paragraph)

and naming types using the symbols from the formalisation. These conventions help

keeping the code compact and also ease the comparison with Tquery, in unison with the

boxed fragments reporting the Tquery syntax in Fig. 2.

We briefly introduce the main elements of Jolie APIs and we comment on the choices

that drove the design of the Jolie/Tquery API. At Lines 1–8 of Fig. 2, we find the definition

of TqueryInterface, the Jolie/Tquery interface. The keyword requestResponse indicates that

the operations associated to it (as a comma-separated list) are synchronous invocations,

where the caller waits for the callee (here, the Jolie/Tquery service) to reply with the

computed response. We defined all the operations of Jolie/Tquery as requestResponses since

this interaction modality matches the invocation semantics of the Tquery operators.

In the syntax of operations, e.g., match(µType)(QueryResponse) at Line 3, we find the name

of the operation (match), the request type between the first parenthesis (µType), and the

response type between the second parenthesis (QueryResponse).

A Jolie type has a name, e.g., QueryResponse at Line 9, and a shape similar to that of the

trees described in ‘The Tquery Formalisation’: a root that contains a value (e.g., bool, int,

string, as well as the empty value, void) and sub-nodes that point to quantified arrays of

typed trees, e.g., the QueryResponse type has a void root (omitted) and a sub-node named

result which points to an unbounded array (*) of elements that can assume any shape

(undefined).

Jolie types can be further refined, e.g., at Line 10, we restrict the set of strings that the

root of the type Path can assume to those matching the regular expression within the regex

predicate, following the definition of paths from Tquery.

Jolie types support sum types (Pierce, 2002, Chapter 11) (Safina et al., 2016) of the shape

type Name: LeftType|RightType. Here, we use sum types to keep the syntax of Tquery

and the structure of Jolie/Tquery types close. For example, at Line 12, we specify that the

type ϕ can either be a boolean, the type ∃p, etc..

The Jolie/Tquery access points and behaviour We now move to the description of the

access points and the behaviour of Jolie/Tquery, reported in Listing 3. In Jolie, a microservice

Giallorenzo et al. (2022), PeerJ Comput. Sci., DOI 10.7717/peerj-cs.1037 24/40

1 inte rface Tq ueryIn t e rface {

2 req u e s tResp o n s e :

3 m atch ( µ Type ) ( Query R espon s e ) ,

4 un win d ( ω Ty pe ) ( Query R e spon s e ) ,

5 pro jec t ( π Type ) ( Quer y R espon s e ) ,

6 gr oup ( γ Ty pe )( Q uery R e spons e ),

7 lo oku p ( λ Ty pe ) ( Que r y Respo n s e )

8 }

For reference, we

report the syntax

of each Tquery op-

erator next to its

types.

9 type QueryR e s pons e : { res ult * : und efin e d }

10 type Pat h : st ring ( r ege x ( " ([A - Za -z_ ] \\ w * \\ .) * ( [A -Za - z_ ] \\ w *)" ) )

p ::= e.p |

11 type µ Type : { data * : un d efin ed qu ery : ϕ }

12 type ϕ : bool | ∃p | E Q_E xp | NOT | AND | OR

13 type ∃p: { exi sts : P ath }

14 type EQ_E xp : { eq ual : E Q_Da ta | EQ_ Path }

15 type E Q_D ata : { d ata * : u ndef ined path : P ath }

16 type E Q_P ath : { l eft : Pa th r ight : Path }

17 type NOT : { not : ϕ }

18 type AND : { and : { le ft : ϕ righ t : ϕ } }

19 type OR : { or : { left : ϕ righ t : ϕ } }

µ(a,ϕ)

ϕ ::= true

| ∃p

| p = a

| p

= p

| ¬

| ϕ ' ϕ

| ϕ ( ϕ

20 type ω Ty pe : { data *: unde fined qu ery : Path }

ω(a, p)

21 type π Type : { data *: unde f ined que ry [1,*]: Π }

22 type Π: Path | I nje ct

23 type Inject : { d stPa th : Pa th value [1,*]: d }

24 type d : any | Valu e Path | Valu e Matc h | Valu e Terna r y

25 type V alue P ath : { path : Path }

26 type V a lueM a tch : { ma tch : ϕ }

27 type V a lueTe r n ary : {

28 condi tion : ϕ

29 ifT rue [ 1,*]: d

30 ifFa lse [1,*]: d

31 }

π(a,Π)

Π ::= p

| d ð p

| p,Π

| d ð p, Π

d ::= b

| p

,. .., d

]

| ϕ

| ϕ?d

: d

32 type γ Ty pe : { data *: und e fine d query : Gro u p_Ex p }

33 type Grou p_Ex p : {

34 agg r egat e * : Γ

35 gro upB y * : Γ

36 }

37 type Γ: { d stPa th : Pa th srcP ath : Path }

γ(a,Γ, Γ

′

)

Γ ::= p

| p ð p

′

| p,Γ

| p ð p

′

,Γ

38 type λ Ty pe : {

39 le ftDa ta * : u ndef i ned

40 le ftPa th : Path

41 ri g htDa ta * : u ndef i ned

42 ri g htPa th : Path

43 d stPa th : Pa th

44 }

λ (a,q, a

′

,r, p)

Figure 2. Mapping between the Tquery operators and Jolie/Tquery API.

19/

PeerJ Comput. Sci. reviewing PDF | (CS-2022:04:72509:1:0:NEW 17 Jun 2022)

Manuscript to be reviewed

Computer Science

Figure 2 Mapping between the Tquery operators and Jolie/Tquery API.

Full-size DOI: 10.7717/peerjcs.1037/fig-2

is identified by the keyword service associated with a name (in Listing 3, Tquery), a set of

access points (in Listing 3, the inputPort at Lines 46–49), and a set of behaviours (in Listing

3, defined through the foreign language (java) at Lines 51–53).

45 s ervice Tquery {

46 inp u t P ort IP {

47 loca t ion : " loc al "

48 interfa c e s : Tq u e r y I n t e r f a c e

Giallorenzo et al. (2022), PeerJ Comput. Sci., DOI 10.7717/peerj-cs.1037 25/40

Jolie access points simplify the definition

of alternative service configurations.

For instance, to expose Jolie/Tquery

as a RESTful service, we need to add a

new inputPort (or change the one

already defined) setting its location

to a socket address (e.g., "socket://

localhost:8080") and its protocol

to http (Montesi, 2016). In general,

protocols in Jolie specify the mapping

between protocol-specific resources and

Jolie operations and their data serialisation.

Since the "local" location transfers

in-memory data structures, the definition

of a protocol is unnecessary.

49 }

51 fo reign java {

52 cl ass : " j oliex . tquer y . engi ne . TqueryService "

53 }

54 }

Listing 3: The Tquery service.

Concerning access points, Jolie provides inputPorts to specify ingress gates, which define

how a service expects clients to invoke its operations, and outputPorts (absent in Listing

3), which specify outbound egress gates for invoking other services (Montesi, Guidi &

Zavattaro, 2014). At Line 46 of Listing 3, we define an inputPort (its name is immaterial

here) with location "local" and interfaces TqueryInterface (cf. Fig. 2). By specifying

an inbound access point with a "local" location, we indicate that our service accepts

in-memory invocations from another Jolie service that runs Jolie/Tquery as an internal

library—through a mechanism called ‘‘embedding’’ (Montesi, Guidi & Zavattaro, 2014).

Regarding behaviours, Jolie provides a high-level language (akin to process calculi

(Montesi, Guidi & Zavattaro, 2014)) to specify the composition of sophisticated workflows

(Gabbrielli, Giallorenzo & Montesi, 2014) through a clean and minimal syntax. Jolie also

supports the specification of behaviours through lower-level languages, like Java and

Javascript, which are useful when integrating/exposing existing libraries as services or to

manage lower-level abstractions like threads and pointers for performance. Jolie/Tquery

falls in the latter category and we implemented its behaviour (and, thus, the Tquery

semantics) using Java. This is visible at Lines 51–53 of Listing 3, where we declare the usage

of the foreign language java to specify the service behaviour (implemented within the

TqueryService class under the class-path joliex.tquery.engine). We omit the presentation

of the Java code, since it closely follows the logic presented in ‘The Tquery Formalisation’.

Extending Jolie/Tquery with query pipelines

Besides providing a faithful implementation of Tquery, we decided to extend Jolie/Tquery

to support multi-stage queries both for reasons of performance and familiarity with the

MongoDB Aggregation framework (MongoDB Inc., 2022).

The extension is minimal and provides an interesting point for showcasing the flexibility

of the Jolie language in evolving existing projects.

Namely, the extension regards the API and the behaviour. We report in Listing 5 the

changes to the Jolie/Tquery API and we omit, as done above, to present the Java code

of the implementation, which is a straightforward sequentialisation of calls to the other

implemented operators.

In the API, we add the pipeline operation among the operations in the TqueryInterface

interface. The new operation requires an associated request type that contains the

specification of the multi-stage queries. Having defined the types of the other operations as

independent components comes in handy. Indeed, the Pipeline type defines its multi-stage

query as an array (under the sub-node pipeline) of subtrees specified through the types

of the other operations. For instance, at Line 11 in Listing 5, a match (Query) stage has the

structure of the ϕ type, which is also the one used by the match operation (in the µType type).

Giallorenzo et al. (2022), PeerJ Comput. Sci., DOI 10.7717/peerj-cs.1037 26/40

Here, the only exception is the type λType, which we did not use for the node lookupQuery,

since the leftData sub-node is absent as the pipeline provides the (left-side) data.

1 i n t e rface T q u e r y I n t e r f a c e {

2 RequestResponse :

3 match ( µType )( Qu e r y R e s p o n s e ),

4 // ...

5 pip e line ( Pipeline )( Q u e r y R e s p o n s e )

6 }

8 type Pipeli n e : {

9 data * : u n defined

10 pip e line [ 1 ,*] :

11 { ma tchQuery : ϕ }

12 | { projectQ u e r y [ 1 , *] : 5 }

13 | { unwi n d Q u e r y : Path }

14 | { gro u p Q u e ry : G r oup_Exp }

15 | { look u p Q u e r y : {

16 left P ath : Pat h

17 right D a t a *: u n d e fined

18 right P a t h : Path

19 dst P ath : P ath

20 }

21 }

22 }

Listing 4: Pipeline support extension (fragments).

The curious reader could wonder why we did not specify the whole Jolie/Tquery interface

through the single pipeline operation. Our point is that, by having both possibilities, users

can opt for the modality that best suits their scenario. For instance, when developing and

debugging a query, it is useful to look at the shape of the single invocations and responses.

Moreover, while pipelines help to make local sequential invocations efficient, they make

the code harder to distribute, since the query now lives as an indivisible data structure.

On the contrary, if we found out that a specific stage of a query, e.g., the match at Line 3

or the unwind at Line 9 of Listing 2, would benefit from scaling it over multiple copies,

we could do that by isolating each operation into a dedicated service and redirecting their

inputs/outputs to perform our original local query as a distributed one. In that case, despite

the architectural change, the logic of the query would remain intact.

The Running Example written in Jolie/Tquery

We conclude this section by presenting the implementation of our running example from

‘Overview and Running Example’, Listing 2. Specifically, we present two alternatives: a

more faithful one in Fig. 3, where we have a one-to-one correspondence between Tquery

operators and Jolie/Tquery operations, and one in Fig. 4 that obtains the same result by

using Jolie/Tquery pipelines.

While the code in Fig. 3 fulfills the promise made in ‘Overview and Running Example’

to show the implementation of the example in Listing 2, we take the chance to illustrate, in

Fig. 4, how one can transition between a composition of single-stage queries to multi-stage,

pipelined ones. Moreover, Fig. 4 is a reference for the actual Jolie/Tquery code used in

‘Benchmarks’ to benchmark our implementation.

Translating Tquery operator calls into Jolie/Tquery ones is straightforward, e.g., the

match at Line 3 of Listing 2 corresponds to Lines 4–11 of Fig. 3. As expected, the main

Giallorenzo et al. (2022), PeerJ Comput. Sci., DOI 10.7717/peerj-cs.1037 27/40

1 get Pat i en t Pse udo I D @ H o spit a lIT ( p D a ta )( pID )

2 ge t Mot ion And T em p er a tu r e @ S m artW a tch ( c r e ds )( _tmp )

4 m a t ch @ Tquer y ( {

5 data << _tmp

6 qu e r y . or << {

7 left < < { e q ual < < { p a t h = " date " d a t a = 202 0 1 128 } }

8 right . or << {

9 left < < { eq u a l << { path = " d a t e " data = 2 0 2 0112 9 } }

10 right << { equal < < { p a t h = " date " data = 2 0201 1 30 } }

11 }} } ) ( resp )

13 g r o up @ Tquer y ( {

14 data << resp . resu l t

15 qu e r y . agg r egat e << { d s t Path = "t " sr c Path = " t" }

16 }) ( resp )

18 p rojec t @ Tquer y ( {

19 data << resp . resu l t

20 qu e r y [0 ] << { d s tPath = " t " val u e . p a t h = " t " }

21 qu e r y [1 ] << { d s tPath = " p a t ien t _id " value = p seud o Id }

22 }) ( tmp )

24 d ete c tFe v er @ H o spi t alIT ( tmp ) ( ha s Feve r )

26 if ( hasF e v er ) {

27 g etS l ee p Pat t er n s @ S m a rtP h one ( cred e nti a ls ) ( _sl )

29 u n w ind @ Tqu e r y ( { data < < _sl query = " M .D .L " } )( resp )

31 p r oject @ T query ( {

32 data < < r e s p . r esult

33 query [0 ] < < { d s tPath = " year " value . path = " y" }

34 query [1 ] < < { d s tPath = " mont h " valu e . p a t h = " M .m " }

35 query [2 ] < < { d s tPath = " day " value . path = " M. D .d " }

36 query [3 ] < < { d s tPath = " qu a lity " v alue . path = " M .D .L . q" }

37 } )( resp )

39 ma t c h @ Tquer y ( {

40 data < < r e s p . r esult

41 query . and < < {

42 left . equal < < { p a t h = " year " data = 2020 }

43 right . and < < {

44 left . e q u al << { path = " m o n th " data = 11 }

45 right . or << {

46 left . e qual < < { pat h = " day " data = 29 }

47 right . equal << { p a t h = " day " data = 30 }

48 } }} }) ( resp )

50 gr o u p @ Tquer y ( {

51 data < < r e s p . r esult

52 query . ag g r ega t e < <{ ds t P ath = " qual i t y " s r c Path = " qual i ty "}

53 } )( resp )

55 p r oject @ T query ( {

56 data < < r e s p . r esult

57 query [0 ] < < { d s tPath = " qu a lity " v alue . path = " qu a lity " }

58 query [1 ] < < { d s tPath = " p a tien t _id " value = p seud o Id }

59 } )( sl )

61 l o o kup @ Tqu e r y ( {

62 leftD a ta < < sl . res u l t le f tPat h = " p ati e nt_i d "

63 righ t Data << tmp . resul t rightP a th = " p a tien t _id "

64 dstPat h = " t emp e rat u res "

65 } )( resp )

67 p r oject @ T query ( {

68 data < < r e s p . r esult

69 query [0 ] < <

70 {d s tPath = " qua l ity " value . path = " qua l i ty " }

71 query [1 ]< <

72 {d s tPath = " te m per a tur e s " v a l ue . path = " te m per a tur e s . t "}

73 query [2 ] < <

74 {d s tPath = " pa t ien t _id " value . path = " p ati e nt_i d "}

75 } )( bs )

77 d et e ctE nce p ha l op a thy @ Hos p ita l IT ( bs )

78 }

Figure 3. Single-stage implementation of Listing 2.

1 get Pat i en t Pse udo I D @ H o spit a lIT ( p D a ta )( pID )

2 ge t Mot ion And T em p er a tu r e @ S m artW a tch ( c r e ds )( _tmp )

4 pt [ i + +] < < { m atc h Quer y . or << {

5 left . eq u a l < < { p ath = " d a te " data = 2 0 2011 2 8 }

6 ri g h t . or << {

7 left . e q ual < < { p a t h = " date " d a t a = 202 0 1 129 }

8 right . equal < < { path = " date " d a t a = 202 0 1130 }

9 }

10 }

12 pt [ i + +] < < {

13 g roup Q uer y . agg r egat e << { d s t Path = "t " sr c P ath = " t" }

14 }

16 pt [ i + +] < < {

17 p roj e ctQ u ery [ 0] < < { d s tPat h = " t " va l u e . p a t h = " t " }

18 p roj e ctQ u ery [ 1] < <

19 { dstPa t h = " p a tie n t_i d " value = pseudo I d }

20 }

22 p ipel i ne @ Tqu e r y ({ data < < _ t m p pi p elin e < < pt} )( tmp )

24 d ete c tFe v er @ H o spi t alIT ( tmp ) ( ha s Feve r )

26 if ( hasF e v er ) {

27 g etS l ee p Pat t er n s @ S m a rtP h one ( cred e nti a ls ) ( _sl )

29 ps [ j] . unw i ndQ u ery = "M .D . L"

31 ps [ j+ +] < < { p roj e ctQ u ery [ 0] < <

32 { dstP a t h = " year " v a l u e . p ath = "y " }

33 pro j ect Q uer y [ 1 ] < <

34 { dstP a t h = " month " value . p a t h = " M . m" }

35 pro j ect Q uer y [ 2 ] < <

36 { dstP a t h = " day " value . path = "M .D . d" }

37 pro j ect Q uer y [ 3 ] < <

38 { dstP a t h = " qua l ity " v a lue . path = "M .D . L. q" }

39 }

41 ps [ j+ +] < < { m atch Q uer y . and < < {

42 left . e q ual << { path = " year " data = 2 0 2 0 }

43 right . and < < {

44 left . equal < < { path = " month " d a t a = 11 }

45 right . or < < {

46 left . e q ual < < { p a t h = " day " data = 29 }

47 right . equal << { p a t h = " day " data = 30 }

48 } }} }

50 ps [ j+ +] . g rou p Que r y . ag g rega t e < <

51 { dstPa t h = " q u ality " srcPat h = " qu a lity " }

53 ps [ j+ +] < < {

54 pro j ect Q uer y [ 0 ] < <

55 { dstP a t h = " qua l ity " v a lue . path = " qua l ity " }

56 pro j ect Q uer y [ 1 ] < <

57 { dstP a t h = " pa t ien t _id " value = p seud o I d }

58 }

60 ps [ j+ +] . l ook u pQu e ry << {

61 righ t Data << tmp . resul t rightP a th = " p a tie n t _id "

62 leftP a th = " p a tie n t_id "

63 dstPat h = " t emp e rat u res "

64 }

66 ps [ j+ +] < < {

67 pro j ect Q uer y [ 0 ] < <

68 { dstP a t h = " qua l ity " v a lue . path = " qua l ity " }

69 pro j ect Q uer y [ 1 ] < <

70 {d s tPath = " te m per a tur e s " v a l ue . path =" t e mpe r atu r es . t" }

71 pro j ect Q uer y [ 2 ] < <

72 { dstP a t h = " pa t ien t _id " value . p a t h = " p a tie n t_id " }

73 }

75 p i peli n e @ Tque r y ( { data << _sl pipelin e << ps }) ( bs )

77 d et e ctE nce p ha l op a thy @ Hos p ita l IT ( bs )

78 }

Figure 4. Multi-stage implementation of Listing 2.

23/30

PeerJ Comput. Sci. reviewing PDF | (CS-2022:04:72509:1:0:NEW 17 Jun 2022)

Manuscript to be reviewed

Computer Science

Figure 3 Single-stage implementation of Listing 2.

Full-size DOI: 10.7717/peerjcs.1037/fig-3

Giallorenzo et al. (2022), PeerJ Comput. Sci., DOI 10.7717/peerj-cs.1037 28/40

1 get Pat i en t Pse udo I D @ H o spit a lIT ( p D a ta )( pID )

2 ge t Mot ion And T em p er a tu r e @ S m artW a tch ( c r e ds )( _tmp )

4 m a t ch @ Tquer y ( {

5 data << _tmp

6 qu e r y . or << {

7 left < < { e q ual < < { p a t h = " date " d a t a = 202 0 1 128 } }

8 right . or << {

9 left < < { eq u a l << { path = " d a t e " data = 2 0 2 0112 9 } }

10 right << { equal < < { p a t h = " date " data = 2 0201 1 30 } }

11 }} } ) ( resp )

13 g r o up @ Tquer y ( {

14 data << resp . resu l t

15 qu e r y . agg r egat e << { d s t Path = "t " sr c Path = " t" }

16 }) ( resp )

18 p rojec t @ Tquer y ( {

19 data << resp . resu l t

20 qu e r y [0 ] << { d s tPath = " t " val u e . p a t h = " t " }

21 qu e r y [1 ] << { d s tPath = " p a t ien t _id " value = p seud o Id }

22 }) ( tmp )

24 d ete c tFe v er @ H o spi t alIT ( tmp ) ( ha s Feve r )

26 if ( hasF e v er ) {

27 g etS l ee p Pat t er n s @ S m a rtP h one ( cred e nti a ls ) ( _sl )

29 u n w ind @ Tqu e r y ( { data < < _sl query = " M .D .L " } )( resp )

31 p r oject @ T query ( {

32 data < < r e s p . r esult

33 query [0 ] < < { d s tPath = " year " value . path = " y" }

34 query [1 ] < < { d s tPath = " mont h " valu e . p a t h = " M .m " }

35 query [2 ] < < { d s tPath = " day " value . path = " M. D .d " }

36 query [3 ] < < { d s tPath = " qu a lity " v alue . path = " M .D .L . q" }

37 } )( resp )

39 ma t c h @ Tquer y ( {

40 data < < r e s p . r esult

41 query . and < < {

42 left . equal < < { p a t h = " year " data = 2020 }

43 right . and < < {

44 left . e q u al << { path = " m o n th " data = 11 }

45 right . or << {

46 left . e qual < < { pat h = " day " data = 29 }

47 right . equal << { p a t h = " day " data = 30 }

48 } }} }) ( resp )

50 gr o u p @ Tquer y ( {

51 data < < r e s p . r esult

52 query . ag g r ega t e < <{ ds t P ath = " qual i t y " s r c Path = " qual i ty "}

53 } )( resp )

55 p r oject @ T query ( {

56 data < < r e s p . r esult

57 query [0 ] < < { d s tPath = " qu a lity " v alue . path = " qu a lity " }

58 query [1 ] < < { d s tPath = " p a tien t _id " value = p seud o Id }

59 } )( sl )

61 l o o kup @ Tqu e r y ( {

62 leftD a ta < < sl . res u l t le f tPat h = " p ati e nt_i d "

63 righ t Data << tmp . resul t rightP a th = " p a tien t _id "

64 dstPat h = " t emp e rat u res "

65 } )( resp )

67 p r oject @ T query ( {

68 data < < r e s p . r esult

69 query [0 ] < <

70 {d s tPath = " qua l ity " value . path = " qua l i ty " }

71 query [1 ]< <

72 {d s tPath = " te m per a tur e s " v a l ue . path = " te m per a tur e s . t "}

73 query [2 ] < <

74 {d s tPath = " pa t ien t _id " value . path = " p ati e nt_i d "}

75 } )( bs )

77 d et e ctE nce p ha l op a thy @ Hos p ita l IT ( bs )

78 }

Figure 3. Single-stage implementation of Listing 2.

1 get Pat i en t Pse udo I D @ H o spit a lIT ( p D a ta )( pID )

2 ge t Mot ion And T em p er a tu r e @ S m artW a tch ( c r e ds )( _tmp )

4 pt [ i + +] < < { m atc h Quer y . or << {

5 left . eq u a l < < { p ath = " d a te " data = 2 0 2011 2 8 }

6 ri g h t . or << {

7 left . e q ual < < { p a t h = " date " d a t a = 202 0 1 129 }

8 right . equal < < { path = " date " d a t a = 202 0 1130 }

9 }

10 }

12 pt [ i + +] < < {

13 g roup Q uer y . agg r egat e << { d s t Path = "t " sr c P ath = " t" }

14 }

16 pt [ i + +] < < {

17 p roj e ctQ u ery [ 0] < < { d s tPat h = " t " va l u e . p a t h = " t " }

18 p roj e ctQ u ery [ 1] < <

19 { dstPa t h = " p a tie n t_i d " value = pseudo I d }

20 }

22 p ipel i ne @ Tqu e r y ({ data < < _ t m p pi p elin e < < pt} )( tmp )

24 d ete c tFe v er @ H o spi t alIT ( tmp ) ( ha s Feve r )

26 if ( hasF e v er ) {

27 g etS l ee p Pat t er n s @ S m a rtP h one ( cred e nti a ls ) ( _sl )

29 ps [ j] . unw i ndQ u ery = "M .D . L"

31 ps [ j+ +] < < { p roj e ctQ u ery [ 0] < <

32 { dstP a t h = " year " v a l u e . p ath = "y " }

33 pro j ect Q uer y [ 1 ] < <

34 { dstP a t h = " month " value . p a t h = " M . m" }

35 pro j ect Q uer y [ 2 ] < <

36 { dstP a t h = " day " value . path = "M .D . d" }

37 pro j ect Q uer y [ 3 ] < <

38 { dstP a t h = " qua l ity " v a lue . path = "M .D . L. q" }

39 }

41 ps [ j+ +] < < { m atch Q uer y . and < < {

42 left . e q ual << { path = " year " data = 2 0 2 0 }

43 right . and < < {

44 left . equal < < { path = " month " d a t a = 11 }

45 right . or < < {

46 left . e q ual < < { p a t h = " day " data = 29 }

47 right . equal << { p a t h = " day " data = 30 }

48 } }} }

50 ps [ j+ +] . g rou p Que r y . ag g rega t e < <

51 { dstPa t h = " q u ality " srcPat h = " qu a lity " }

53 ps [ j+ +] < < {

54 pro j ect Q uer y [ 0 ] < <

55 { dstP a t h = " qua l ity " v a lue . path = " qua l ity " }

56 pro j ect Q uer y [ 1 ] < <

57 { dstP a t h = " pa t ien t _id " value = p seud o I d }

58 }

60 ps [ j+ +] . l ook u pQu e ry << {

61 righ t Data << tmp . resul t rightP a th = " p a tie n t _id "

62 leftP a th = " p a tie n t_id "

63 dstPat h = " t emp e rat u res "

64 }

66 ps [ j+ +] < < {

67 pro j ect Q uer y [ 0 ] < <

68 { dstP a t h = " qua l ity " v a lue . path = " qua l ity " }

69 pro j ect Q uer y [ 1 ] < <

70 {d s tPath = " te m per a tur e s " v a l ue . path =" t e mpe r atu r es . t" }

71 pro j ect Q uer y [ 2 ] < <

72 { dstP a t h = " pa t ien t _id " value . p a t h = " p a tie n t_id " }

73 }

75 p i peli n e @ Tque r y ( { data << _sl pipelin e << ps }) ( bs )

77 d et e ctE nce p ha l op a thy @ Hos p ita l IT ( bs )

78 }

Figure 4. Multi-stage implementation of Listing 2.

23/30

PeerJ Comput. Sci. reviewing PDF | (CS-2022:04:72509:1:0:NEW 17 Jun 2022)

Manuscript to be reviewed

Computer Science

Figure 4 Multi-stage implementation of Listing 2.

Full-size DOI: 10.7717/peerjcs.1037/fig-4

Giallorenzo et al. (2022), PeerJ Comput. Sci., DOI 10.7717/peerj-cs.1037 29/40

difference is that we need to map the elements of the criterion ϕ from Line 3 of Listing 2

into a Jolie tree that follows the shape of type ϕ (cf. Lines 11–19 of Fig. 2).

The reuse of the types of the single-stage operators in the definition of the pipeline helps

migrating between the two modalities. For example, at Lines 4–10 of Fig. 4, we find that

the definition of the match stage under the pt data structure follows the one at Lines 4–11

of Fig. 3.

We finally show how our implementation can interact with different services and

heterogeneous data sources. In particular, we assume that the service offered by the hospital

communicates XML messages over HTTP, and that smart-watches instead use an efficient

binary protocol—SODEP (Montesi, Guidi & Zavattaro, 2014). These assumptions are

coded in Jolie for our example with appropriate outputPorts that allow our implementation

to contact these other components by using the right transports and data formats, as

follows (we parameterise our code on the locations of these components, which are

provided externally).

1 o u t p u t Port H ospitalIT {

2 loc a tion : param s . h o s p i t a l L o c ation

3 pro t ocol : h ttp { fo rmat = " xml " }

4 inter f a c e s : H ospitalInter f a c e

5 }

7 o u t p u t Port S martWatch {

8 loc a tion : param s . s m a r t W a tchLocati o n

9 pro t ocol : sodep

10 inter f a c e s : S martWatc h I n t e r f a ce

11 }

Listing 5: Collecting data from heterogeneous sources.

The rest of our implementation is modular to these details: changing locations, protocols,

or data formats does not require changing the code shown in Figs. 3 and 4.

BENCHMARKS

We now present the method we followed to benchmark Jolie/Tquery and our experimental

results. Specifically, we concentrate on the main application scenario of Tquery, i.e., that of

ephemeral data-handling, exemplified in ‘Overview and Running Example’ with the query

logic presented in Listing 2. In ‘The Running Example written in Jolie/Tquery’ we showed

two possible concrete realisations of the logic in Listing 2, developed using Jolie/Tquery.

Here, we use Listing 2 as use case for our benchmarks and, as motivated below, the pipeline

Jolie/Tquery realisation of Listing 2 (from Fig. 4), as the reference implementation to run

our experiments.

To obtain a baseline against which to contrast the performance of Jolie/Tquery,

we develop an alternative implementation of the example at ‘Overview and Running

Example’ that uses MongoDB. This alternative implementation is the closest we can

obtain to the logic expressed in ‘Overview and Running Example’, since i) the MongoDB

query language (MongoDB Inc., 2022) inspired (via (Botoeva et al., 2018)) the design

of Tquery and ii) the former supports a superset of the operators of the latter. As a

confirmation of this fact, we implemented the logic of Listing 2 using MongoDB as a

Giallorenzo et al. (2022), PeerJ Comput. Sci., DOI 10.7717/peerj-cs.1037 30/40

Through the jolie-mongodb-driver

library, available at https://github.com/

szingaro/jmdb, which uses the MongoDB

synchronous Java library, available at

https://docs.mongodb.com/drivers/java/

sync/current/.

sequence of two, multi-stage queries, issued through the ‘‘aggregate’’ MongoDB API

(https://docs.mongodb.com/manual/aggregation/). The resulting implementation follows

the same invocation pattern as the one presented in ‘The Running Example written in

Jolie/Tquery’, which uses the pipeline API extension of Jolie/Tquery, thus, motivating our

choice to use this variant.

We remark that MongoDB provides an ‘‘in-memory’’ modality that avoids the overhead

of making the data persistent on disk. Using this modality would likely give us baseline

values closer to the in-memory performance profile of Tquery. Unfortunately, this modality

is accessible only through the paid MongoDB Enterprise Advanced Subscription. Since

using a paid-only feature would hinder the reproducibility of our experiments, we do not

consider it. Here, we consider three configurations for MongoDB. First, the default one,

tailored for persistency, that writes logs of transactions and data on disk. The second one is

the MongoDB in the ‘‘no journal’’ modality, which avoids to write a log of the transactions

on disk. The third one is an ephemeral configuration taken from grey literature (Girbal,

2021) that combines the ‘‘no journal’’ modality with the usage of a tmpfs (Snyder, 1990)

disk as the one where MongoDB stores its data, to avoid the latencies of writing on

non-volatile storage.

Below, we report the respective performance of the four configurations—one for

Jolie/Tquery and three for MongoDB—in terms of the delay between when the engine

receives a request and when it is ready to send back the response. Hence, we avoid recording

the time spent transmitting the data between the invoker and the data-handling engine,

which is orthogonal to the engine’s performance.

To run our benchmarks, we developed two Jolie microservices: one, called

TqueryService, which contains the implementation in Fig. 4 and the other, called

MongoService, which implements the following behaviour: (i) insert the data in MongoDB,

(ii) perform the queries through MongoDB, and (iii) drop the data from MongoDB, to

ensure ephemerality. When recording the performance of MongoService, we include

the deletion (drop) time, before issuing back the response. To let MongoService and

MongoDB interact, we use the synchronous version of the MongoDB Java Drivers

and we

implement its behaviour in Java, similarly as done in ‘The implementation of Jolie/Tquery’

for Jolie/Tquery.

We synthetically generate 5 tiers of data for the benchmarks. Specifically, we generate

5 pairs of JSON files, each including one file for the temperatures and one for the sleep

logs, following the structures from Listing 1. Each tier covers one year of recordings and

it includes a number of samplings per day that doubles from a tier to the next: for the

temperatures, the first tier contains one sampling per minute (1440 samplings per day),

the second contains two samplings per minute (2880), and so on; for the sleep logs, the

first tier contains 16 samplings per day, the second contains 32, and so on.

Our benchmark architecture includes a third Jolie microservice, called DataLoader,

which we use to implement the high-level benchmark logic reported in Algorithm 1.

Essentially, given the number of invocations to perform (min_total_calls), the number of

requests in a batch (batch_size), and the set of data tiers (tiers), the service sends a sequence

of min_total_calls/batch_size batches (rounded up to the next largest integer, to make sure

Giallorenzo et al. (2022), PeerJ Comput. Sci., DOI 10.7717/peerj-cs.1037 31/40

to issue at least min_total_calls invocations). In Algorithm 1, the call invokeTargetService

performs, in parallel, as many queries as indicated by the batch_size, where ‘‘Target ’’ is one

of the four configurations of our benchmark.

Algorithm 1: The DataLoader service logic.

Input: min_total_calls, batch_size, tiers

begin

for tier in tiers do

for batch ← 0 to ceil( min_total_calls / batch_size ) do

invokeTargetService ( tier, batch_size )

end

We execute our benchmarks on a machine equipped with an Intel Xeon Silver 4208

CPU @ 2.10 GHz (32 CPUs), 96GB RAM, and a Dell FH49G SSD. The machine runs

CentOS 7 (Kernel 3.10.0 x86_64), Java 11 (with maximal heap size of 32GB), Jolie 1.10.5,

Jolie/Tquery 0.4.10, the MongoDB Synchronous Driver 4.2.3, and MongoDB Community

Server 4.4.6.

We report in Fig. 5 our benchmarks of Jolie/Tquery and MongoDB, aggregated per

batch size (from the top-left corner, for 5, 8, 10, 12, 15, and 20 parallel requests):

each plot represents the relation between the data-tier size and the average execution

time, maintaining constant the number of parallel invocations. The experimental results

show that Jolie/Tquery performs consistently faster than MongoDB (all configurations).

Since in the test cases with MongoDB we record the request-to-response delay of the

database, the higher execution times of these cases correspond to both the overhead of

the communication and the possible bottlenecks due to establishing connections to it. We

notice a slight decrease in the relative distance between Jolie/Tquery and MongoDB at the

increase of batch and data-tier sizes (in particular, the fourth and fifth tiers and the 15-

and 20-sized batches). Our intuition of the phenomenon is that, on the given machine,

when exceeding those thresholds, the Jolie execution runtime and the Jolie/Tquery engine

undergo overhead due to resource contention. As expected, the default configuration of

MongoDB is the one that performs the worst. The other two configurations (‘‘no journal’’

and ‘‘no journal in-memory’’) perform slightly better than the default and the difference

between them is negligible—our intuition is that writing on disk is the driving factor that

determines the drop in performance.

For completeness, we report in Fig. 6 the benchmarks aggregated by engine, which

confirm the observations above: Jolie/Tquery consistently outperforms MongoDB over the

different batches, where the degree of parallelism and the size of data are the main factors

that determine changes in the performance trend.

Besides the direct results commented above, the performance behaviour plotted in Fig.

5 and Fig. 6 indicate that, when reaching some empirical threshold values, the system

Giallorenzo et al. (2022), PeerJ Comput. Sci., DOI 10.7717/peerj-cs.1037 32/40

Java 11 (with maximal heap size of 32GB), Jolie 1.10.5, Jolie/Tquery 0.4.10, the MongoDB Synchronous

943

Driver 4.2.3, and MongoDB Community Server 4.4.6.944

Jolie/Tquery MongoDB MongoDB no journal MongoDB no journal, in-memory

1 2 3 4

Tiers

Average execution time (seconds)

Batch size 5

1 2 3 4

Batch size 8

1 2 3 4

Batch size 10

1 2 3 4

Tiers

Average execution time (seconds)

Batch size 12

1 2 3 4

Batch size 15

1 2 3 4

Batch size 20

Figure 5. Batch-wise benchmarks for Jolie/Tquery and MongoDB.

1 2 3 4

Tiers

Average execution time (seconds)

1 2 3 4

Tiers

Average execution time (seconds)

1 2 3 4

Figure 6. Engine-wise benchmarks of MongoDB (top-left), MongoDB without journaling (top-right),

MongoDB without journaling and in-memory (bottom-left), and Jolie/Tquery (bottom-right). The lines

represent the different batches of requests.

We report in Figure 5 our benchmarks of Jolie/Tquery and MongoDB, aggregated per batch size

945

(from the top-left corner, for 5, 8, 10, 12, 15, and 20 parallel requests): each plot represents the relation

946

between the data-tier size and the average execution time, maintaining constant the number of parallel

947

invocations. The experimental results show that Jolie/Tquery performs consistently faster than MongoDB

948

25/30

PeerJ Comput. Sci. reviewing PDF | (CS-2022:04:72509:1:0:NEW 17 Jun 2022)

Manuscript to be reviewed

Computer Science

Figure 5 Batch-wise benchmarks for Jolie/Tquery and MongoDB.

Full-size DOI: 10.7717/peerjcs.1037/fig-5

would benefit from scaling-up, either by distributing the query over multiple nodes or by

having multiple copies of the same service and balancing the requests. Here, the flexibility

of Jolie/Tquery can help the user to attain those configurations by minimising the footprint

of the migration on both the system (no need to deploy additional database instances) and

the codebase (cf. ‘Extending Jolie/Tquery with query pipelines’).

DISCUSSION AND CON CLUSION

In this article, we presented Tquery, which is a theory for querying semi-structured

data, compatible with Jolie. While Tquery is a formal model for general reference, we

also presented Jolie/Tquery, which we showed to be especially suitable in the context

of ephemeral data-handling. However, Jolie/Tquery is useful in general, for example in

big-data analytics scenarios, where developers can specifies queries in a single node and

then easily distribute it over different nodes.

Looking at future extensions, a natural evolution of this work is to perform a more

complete evaluation of the expressivity of Jolie/Tquery by implementing well-known data-

flow patterns (Hohpe & Woolf, 2004). A useful by-product of that endevour is the collection

of a library of data-flow patterns implemented in Jolie/Tquery, available to developers. A

complementary contribution to the above proposal is to perform an exhaustive study and

benchmarking of the technologies for ephemeral data-handling. In that work, we would

start by collecting real-world use cases of ephemeral data-handling and by selecting the

most representative ones into a library of test scenarios. Then, we would collect the main

tools used in ephemeral data-handling contexts (including Jolie/Tquery) and compare

them from the different points of view of the features they have and their efficiency (e.g., in

Giallorenzo et al. (2022), PeerJ Comput. Sci., DOI 10.7717/peerj-cs.1037 33/40

Java 11 (with maximal heap size of 32GB), Jolie 1.10.5, Jolie/Tquery 0.4.10, the MongoDB Synchronous

943

Driver 4.2.3, and MongoDB Community Server 4.4.6.944

Jolie/Tquery MongoDB MongoDB no journal MongoDB no journal, in-memory

1 2 3 4

Tiers

Average execution time (seconds)

Batch size 5

1 2 3 4

Batch size 8

1 2 3 4

Batch size 10

1 2 3 4

Tiers

Average execution time (seconds)

Batch size 12

1 2 3 4

Batch size 15

1 2 3 4

Batch size 20

Figure 5. Batch-wise benchmarks for Jolie/Tquery and MongoDB.

1 2 3 4

Tiers

Average execution time (seconds)

1 2 3 4

Tiers

Average execution time (seconds)

1 2 3 4

Figure 6. Engine-wise benchmarks of MongoDB (top-left), MongoDB without journaling (top-right),

MongoDB without journaling and in-memory (bottom-left), and Jolie/Tquery (bottom-right). The lines

represent the different batches of requests.

We report in Figure 5 our benchmarks of Jolie/Tquery and MongoDB, aggregated per batch size

945

(from the top-left corner, for 5, 8, 10, 12, 15, and 20 parallel requests): each plot represents the relation

946

between the data-tier size and the average execution time, maintaining constant the number of parallel

947

invocations. The experimental results show that Jolie/Tquery performs consistently faster than MongoDB

948

25/30

PeerJ Comput. Sci. reviewing PDF | (CS-2022:04:72509:1:0:NEW 17 Jun 2022)

Manuscript to be reviewed

Computer Science

Figure 6 Engine-wise benchmarks of MongoDB (top-left), MongoDB without journaling (top-right),

MongoDB without journaling and in-memory (bottom-left), and Jolie/Tquery (bottom-right). The

lines represent the different batches of requests.

Full-size DOI: 10.7717/peerjcs.1037/fig-6

terms of program size) and performance as obtained through the implementation of our

library of tests.

Another direction is widening the scope of application of Jolie/Tquery with case

studies and experiments where data queries are performed by low-power devices in IoT

environments. This would entail building topologies of nodes with different tasks—e.g.,

gatherers (e.g., edge devices equipped with sensors), collectors (e.g., fog nodes that use

Jolie/Tquery to aggregate and forward the gathered data to more powerful nodes), and

crunchers (e.g., cloud nodes where Jolie/Tquery would manage the high amount of data

coming from the edge and fog layers)—and benchmarking their performance (possibly

in comparison with alternative technologies for ephemeral data handling). Querying data

on devices with low power and memory would likely require implementing strategies for

distributing Jolie/Tquery pipelines over networks; future work in this direction will be able

to benefit from the native support for services in heterogeneous environments offered by

Jolie, which was another reason for developing a querying framework for Jolie.

We think that the above studies, besides providing us with the necessary material to guide

us in evolving Jolie/Tquery—e.g., indicating the need for the inclusion of new operators—,

Giallorenzo et al. (2022), PeerJ Comput. Sci., DOI 10.7717/peerj-cs.1037 34/40

would generate useful references for researchers to orient themselves in the growing field

of ephemeral data-handling.

While studying the Tquery operators, we noticed and reported on how the shape of

the data impacts on the possibility to distribute the stages of the query pipeline. To the

best of our knowledge, this is a design space that did not receive a lot of attention in the

literature and, yet, we deem it fundamental to provide further means for improving the

performance of ephemeral data-handling systems. Here, our intuition is that Jolie types

can help in providing a model that we can use to reason on the shape of the data and their

interplay with the operators in a given query. Possible outcomes of this study include giving

guidelines to developers to maximise the flexibility of their queries, as well as implementing

tools that automatise the optimal distribution of query pipelines.

Finally, since Jolie/Tquery come as a library for the Jolie language, by implementing

the support for new data formats in Jolie we would make them automatically available for

Jolie/Tquery users.

ACKNOWLEDGEMENTS

The authors thank Claudio Guidi and Balint Maschio for useful discussions on the practical

motivation of our framework.

ADDITIONAL INFORMATION AND DECLARATIONS

Funding

This work was sponsored by Villum Fonden, grant no. 29518, by Independent Research

Fund Denmark, grant no. 0135-00219, and by Horizon2020, grant no. 825619. The funders

had no role in study design, data collection and analysis, decision to publish, or preparation

of the manuscript.

Grant Disclosures

The following grant information was disclosed by the authors:

Villum Fonden: 29518.

Independent Research Fund Denmark: 0135-00219.

Horizon2020: 825619.

Competing Interests

The authors declare there are no competing interests.

Author Contributions

• Saverio Giallorenzo conceived and designed the experiments, performed the

experiments, analyzed the data, performed the computation work, prepared figures

and/or tables, authored or reviewed drafts of the article, and approved the final draft.

• Fabrizio Montesi conceived and designed the experiments, performed the experiments,

analyzed the data, performed the computation work, authored or reviewed drafts of the

article, and approved the final draft.

Giallorenzo et al. (2022), PeerJ Comput. Sci., DOI 10.7717/peerj-cs.1037 35/40

• Larisa Safina conceived and designed the experiments, performed the experiments,

analyzed the data, performed the computation work, prepared figures and/or tables,

authored or reviewed drafts of the article, and approved the final draft.

• Stefano Pio Zingaro conceived and designed the experiments, performed the

experiments, analyzed the data, performed the computation work, prepared figures

and/or tables, authored or reviewed drafts of the article, and approved the final draft.

Data Availability

The following information was supplied regarding data availability:

The code (test data is included in the tests directory) is available at GitHub: Available at

https://github.com/jolie/tquery.

Supplemental Information

Supplemental information for this article can be found online at http://dx.doi.org/10.7717/

peerj-cs.1037#supplemental-information.

REFERENCES

Apache. 2005. Apache CouchDB. Available at https://couchdb.apache.org .

Apache. 2022a. Apache Flink. Available at https://flink.apache.org .

Apache. 2022b. Apache Samza. Available at https://samza.apache.org .

Apache. 2022c. Apache Storm. Available at https://storm.apache.org .

Arasu A, Babcock B, Babu S, Cieslewicz J, Datar M, Ito K, Motwani R, Srivastava

U, Widom J. 2016. STREAM: the stanford data stream management system.

In: Garofalakis M, Gehrke J, Rastogi R, eds. Data stream management: processing

high-speed data streams. Berlin, Heidelberg: Springer Berlin Heidelberg, 317–336

DOI 10.1007/978-3-540-28608-0_16.

Arasu A, Babu S, Widom J. 2006. The CQL continuous query language: seman-

tic foundations and query execution. The VLDB Journal 15(2):121–142

DOI 10.1007/s00778-004-0147-z.

Armbrust M, Fox A, Griffith R, Joseph AD, Katz R, Konwinski A, Lee G, Patterson D,

Rabkin A, Stoica I. 2010. A view of cloud computing. Communications of the ACM

53(4):50–58.

Babcock B, Babu S, Datar M, Motwani R, Widom J. 2002. Models and issues in data

stream systems. In: Proceedings of the twenty-first ACM SIGMOD-SIGACT-SIGART

symposium on principles of database systems, PODS ’02. New York, NY, USA:

Association for Computing Machinery, 1–16 DOI 10.1145/543613.543615.

Babu S, Widom J. 2001. Continuous queries over data streams. SIGMOD Record

30(3):109–120 DOI 10.1145/603867.603884.

Baker SB, Xiang W, Atkinson I. 2017. Internet of Things for smart Healthcare:

technologies, challenges, and opportunities. IEEE Access 5:26521–26544

DOI 10.1109/ACCESS.2017.2775180.

Barbieri DF, Braga D, Ceri S, Della Valle E, Grossniklaus M. 2009. C-SPARQL: sPARQL

for continuous querying. In: Proceedings of the 18th international conference on world

Giallorenzo et al. (2022), PeerJ Comput. Sci., DOI 10.7717/peerj-cs.1037 36/40

wide web, WWW ’09. New York, NY, USA: Association for Computing Machinery,

1061–1062 DOI 10.1145/1526709.1526856.

Botoeva E, Calvanese D, Cogrel B, Rezk M, Xiao G. 2016. A formal presentation of

MongoDB (Extended Version), CoRR. ArXiv preprint. arXiv:1603.09291.

Botoeva E, Calvanese D, Cogrel B, Xiao G. 2018. Expressivity and complexity of

MongoDB queries. In: Kimelfeld B, Amsterdamer Y, eds. 21st International

conference on database theory, ICDT 2018, March 26-29, 2018, Vienna, Austria,

volume 98 of LIPIcs. Schloss Dagstuhl - Leibniz-Zentrum für Informatik, 9:1–9:23

DOI 10.4230/LIPIcs.ICDT.2018.9.

Bray T, Paoli J, Sperberg-McQueen CM, Maler E, Yergeau F. 2000. Extensible markup

language (XML) 1.0, W3C recommendation October. Cambridge, MA, USA: W3C.

Brian Krebs. 2017. Extortionists wipe thousands of databases, victims who pay up get

stiffed. Available at https://krebsonsecurity.com/2017/01/extortionists-wipe-thousands-

of-databases-victims-who-pay-up-get-stiffed.

Bunn JA, Navalta JW, Fountaine CJ, Reece JD. 2018. Current state of commercial

wearable technology in physical activity monitoring 2015-2017. International Journal

of Exercise Science 11(7):503–515.

Callegati F, Gabbrielli M, Giallorenzo S., Melis A, Prandini M. 2017. Smart mobility

for all: a global federated market for mobility-as-a-service operators. In: 20th IEEE

international conference on intelligent transportation systems, ITSC 2017, Yokohama,

Japan, October 16-19, 2017. Piscataway: IEEE, 1–8 DOI 10.1109/itsc.2017.8317701.

Caspi P, Pilaud D, Halbwachs N, Plaice JA. 1987. LUSTRE: a declarative language

for real-time programming. In: Proceedings of the 14th ACM SIGACT-SIGPLAN

symposium on principles of programming languages, POPL ’87. New York, NY, USA:

Association for Computing Machinery, 178–188 DOI 10.1145/41625.41641.

Chen J, DeWitt DJ, Tian F, Wang Y. 2000. NiagaraCQ: a scalable continuous query sys-

tem for internet databases. In: Proceedings of the 2000 ACM SIGMOD international

conference on management of data, SIGMOD ’00. New York, NY, USA: Association

for Computing Machinery, 379–390 DOI 10.1145/342009.335432.

Cheney J, Lindley S, Wadler P. 2013. A practical theory of language-integrated query.

ACM SIGPLAN Notices 48(9):403–416.

Crockford D. 2006. The application/json media type for javascript object notation (json).

Available at http://www.ietf.org/rfc/rfc4627.txt.

Diao Y, Fischer P, Franklin M, To R. 2002. YFilter: efficient and scalable filtering of

XML documents. In: Proceedings 18th international conference on data engineering.

341–342 DOI 10.1109/ICDE.2002.994748.

Dragoni N, Giallorenzo S, Lluch-Lafuente A, Mazzara M, Montesi F, Mustafin R, Safina

L. 2017. Microservices: yesterday, today, and tomorrow. In: Present and ulterior

software engineering. Berlin, Germany: Springer, 195–216

DOI 10.1007/978-3-319-67425-4_12.

Elasticsearch. 2022. Elasticsearch event query language. Available at https://www.elastic.

co/blog/introducing-event-query-language.

Ellis T. 2014. Opaleye. Available at https://github.com/tomjaguarpaw/haskell-opaleye.

Giallorenzo et al. (2022), PeerJ Comput. Sci., DOI 10.7717/peerj-cs.1037 37/40

Esteves S, Janssens N, Theeten B, Veiga L. 2017. Empowering stream processing through

edge clouds. SIGMOD Rec. 46(3):23–28 DOI 10.1145/3156655.3156661.

Fussel M. 1997. Foundations of object-relational mapping. Available at http://markfussell.

emenar.com/blog/object-relational/.

Gabbrielli M, Giallorenzo S, Lanese I, Zingaro SP. 2018. A language-based approach for

interoperability of IoT platforms. Available at https://scholarspace.manoa.hawaii.edu/

server/api/core/bitstreams/573255ff-bc3a-4928-9f5b-3809a37745c3/content .

Gabbrielli M, Giallorenzo S, Lanese I, Zingaro SP. 2019. Linguistic abstractions for

interoperability of IoT platforms. In: Majchrzak T, Mateos C, Poggi F, Grønli TM,

eds. Towards integrated web, mobile, and IoT technology. Lecture notes in business in-

formation processing, vol. 347. Cham: Springer DOI 10.1007/978-3-030-28430-5_5.

Gabbrielli M, Giallorenzo S, Montesi F. 2014. Service-oriented architectures: from

design to production exploiting workflow patterns. In: Omatu S, Bersini H, Cor-

chado J, Rodríguez S, Pawlewski P, Bucciarelli E, eds. Distributed computing and

artificial intelligence, 11th international conference. Advances in intelligent systems and

computing, vol 290. Cham: Springer, DOI 10.1007/978-3-319-07593-8_17.

Giallorenzo S, Montesi F, Peressotti M, Rademacher F, Sachweh S. 2021. Jolie and

LEMMA: Model-Driven Engineering and Programming Languages Meet on

Microservices. In: Damiani F, Dardha O, eds. Coordination models and languages.

COORDINATION 2021. Lecture notes in computer science(), vol 12717. Cham:

Springer, DOI 10.1007/978-3-030-78142-2_17.

Giallorenzo S, Montesi F, Safina L, Zingaro SP. 2019. Ephemeral data handling in

microservices. In: Bertino E, Chang CK, Chen P, Damiani E, Goul M, Oyama K, eds.

2019 IEEE international conference on services computing, SCC 2019, Milan, Italy, July

8-13, 2019. Piscataway: IEEE, 234–236 DOI 10.1109/SCC.2019.00048.

Girbal A. 2021. How to use MongoDB as a pure in-memory DB. Available at https://

edgystuff.tumblr.com/post/49304254688/how-to-use-mongodb-as-a-pure-in-memory-

db-redis (accessed on 20 September 2021).

Hirten RP, Danieletto M, Tomalin L, Choi KH, Zweig M, Golden E, Kaur S, Helmus

D, Biello A, Pyzik R. 2020. Longitudinal physiological data from a wearable device

identifies SARS-CoV-2 infection and symptoms and predicts COVID-19 diagnosis.

MedRxiv DOI 10.1101/2020.11.06.20226803.

Hirzel M, Schneider S, Gedik B. 2017. SPL: an extensible language for distributed stream

processing. ACM Transactions on Programming Languages and Systems 39(1):5

DOI 10.1145/3039207.

Hohpe G, Woolf B. 2004. Enterprise integration patterns: designing, building, and deploy-

ing messaging solutions. Boston: Addison-Wesley Professional.

Jang M. 2006. Linux annoyances for geeks: getting the most flexible system in the world just

the way you want it. Sebastopol, CA, USA: O’Reilly Media.

Kong L, Mamouras K. 2020. StreamQL: a query language for processing stream-

ing time series. Proceedings of the ACM on Programming Languages 183:1–32

DOI 10.1145/3428251.

Giallorenzo et al. (2022), PeerJ Comput. Sci., DOI 10.7717/peerj-cs.1037 38/40

Leavitt N. 2010. Will NoSQL databases live up to their promise? Computer 43:12–14

DOI 10.1109/mc.2010.58.

Ma M, Wang P, Chu C-H. 2013. Data management for internet of things: challenges,

approaches and opportunities. In: 2013 IEEE International conference on green

computing and communications and IEEE Internet of Things and IEEE cyber, physical

and social computing. Piscataway: IEEE, 1144–1151.

Maschio B. 2017. The use of microservices to implement cross process integration and

data sharing. Available at https://www.conf-micro.services/2017/papers/Maschio.pdf .

Maschio B. 2019. Updating the current Jolie microservices based Document Manage-

ment System to include electronic invoicing. In: Proceedings of the 2th international

conference on microservices. Available at https://www.conf-micro.services/2019/papers/

Microservices_2019_paper_15.pdf .

Meijer E, Beckman B, Bierman G. 2006. Linq: reconciling object, relations and xml in

the. net framework. In: Sigmod. 706–706.

Mendell M, Nasgaard H, Bouillet E, Hirzel M, Gedik B. 2012. Extending a general-

purpose streaming system for XML. In: Proceedings of the 15th international confer-

ence on extending database technology. 534–539.

MongoDB Inc. 2018a. MongoDB aggregation framework. Available at https://www.

mongodb.com/developer/products/mongodb/aggregation-framework/.

MongoDB Inc. 2018b. MongoDB website. Available at https://www.mongodb.com/.

MongoDB Inc. 2022. Aggregation pipeline operators in MongoDB. Available at https:

//docs.mongodb.com/manual/reference/operator/aggregation/.

Montesi F. 2016. Process-aware web programming with Jolie. Science of Computer

Programming 130:69–96 DOI 10.1016/j.scico.2016.05.002.

Montesi F, Guidi C, Zavattaro G. 2014. Service-oriented programming with Jolie.

In: Bouguettaya A, Sheng Q, Daniel F, eds. Web services foundations. New York:

Springer DOI 10.1007/978-1-4614-7518-7_4.

Mostert M, Bredenoord AL, Biesaart MC, Van Delden JJ. 2016. Big Data in medical

research and EU data protection law: challenges to the consent or anonymise

approach. European Journal of Human Genetics 24(7):956–960

DOI 10.1038/ejhg.2015.239.

Narkhede N. 2017. Introducing KSQL: streaming SQL for Apache Kafka. Available at

https://www.confluent.io/blog/ksql-streaming-sql-for-apache-kafka/.

Oram A. 2019. Ballerina: a language for network-distributed applications. Sebastopol, CA,

USA: O’Reilly Media, Incorporated.

Pierce BC. 2002. Types and programming languages. Cambridge: MIT Press.

Purohit B, Kumar A, Mahato K, Chandra P. 2020. Smartphone-assisted personalized

diagnostic devices and wearable sensors. Current Opinion in Biomedical Engineering

13:42–50 DOI 10.1016/j.cobme.2019.08.015.

Reda R, Piccinini F, Carbonaro A. 2018. Towards consistent data representation in the

IoT healthcare landscape. In: DH ’18: Proceedings of the 2018 international conference

on digital health. DOI 10.1145/3194658.3194668.

Giallorenzo et al. (2022), PeerJ Comput. Sci., DOI 10.7717/peerj-cs.1037 39/40

Ron A, Shulman-Peleg A, Puzanov A. 2016. Analysis and mitigation of NoSQL injec-

tions. IEEE Security & Privacy 14(2):30–39 DOI 10.1109/MSP.2016.36.

Rose N. 2014. The human brain project: social and ethical challenges. Neuron

82(6):1212–1215 DOI 10.1016/j.neuron.2014.06.001.

Safina L, Mazzara M, Montesi F, Rivera V. 2016. Data-driven workflows for microser-

vices: genericity in Jolie. In: Barolli L, Takizawa M, Enokido T, Jara AJ, Bocchi

Y, eds. 30th IEEE international conference on advanced information networking

and applications, AINA 2016, Crans-Montana, Switzerland, 23-25 March, 2016.

Piscataway: IEEE Computer Society, 430–437 DOI 10.1109/aina.2016.95.

Shein E. 2013. Ephemeral Data. Communications of the ACM 56(9):20–22.

Shi W, Cao J, Zhang Q, Li Y, Xu L. 2016. Edge computing: vision and challenges. IEEE

Internet of Things Journal 3(5):637–646 DOI 10.1109/JIOT.2016.2579198.

Siddhi. 2022. Siddhi Streaming SQL. Available at https://siddhi.io/en/v4.x/docs/query-

guide/.

Snyder P. 1990. tmpfs: a virtual memory file system. In: Proceedings of the autumn 1990

European UNIX Users’ group conference. 241–248.

Thurman SM, Wasylyshyn N, Roy H, Lieberman G, Garcia JO, Asturias A, Okafor

GN, Elliott JC, Giesbrecht B, Grafton ST, Mednick SC, Vettel JM. 2018. Individual

differences in compliance and agreement for sleep logs and wrist actigraphy: a

longitudinal study of naturalistic sleep in healthy adults. PLOS ONE 13(1):e0191883

DOI 10.1371/journal.pone.0191883.

Tommasini R, Sakr S, Balduini M, Valle ED. 2019. An outlook to declarative languages

for big steaming data. In: Proceedings of the 13th ACM international conference on

distributed and event-based systems, DEBS ’19. New York, NY, USA: Association for

Computing Machinery, 199–202 DOI 10.1145/3328905.3332462.

Van Alsenoy B. 2019. General data protection regulation. In: Data Protection Law in the

EU: roles, responsibilities and liability. Cambridge: Intersentia, 279–324.

Vigevano F, Liso PD. 2018. Chapter 11 - differential diagnosis. In: Acute encephalopathy

and encephalitis in infancy and its related disorders. Amsterdam: Elsevier, 81–85.

Visveswaran S. 2000. Dive into connection pooling with J2EE. Available at https://www.

infoworld.com/article/2076221/dive-into-connection-pooling-with-j2ee.html.

W3c. 2001. Transport message exchange pattern: single-Request-Response. Available at

https://www.w3.org/2000/xp/Group/1/10/11/2001-10-11-SRR-Transport_MEP.

WSO2. 2022. WSO2 stream processor. Available at https://wso2.com/integration/

streaming-integrator/.

Giallorenzo et al. (2022), PeerJ Comput. Sci., DOI 10.7717/peerj-cs.1037 40/40